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| "title": "Language Models are Few-Shot Learners" | |
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| "arxivId": "2103.00020", | |
| "title": "Learning Transferable Visual Models From Natural Language Supervision" | |
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| "arxivId": "1312.6199", | |
| "title": "Intriguing properties of neural networks" | |
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| "arxivId": "2203.02155", | |
| "title": "Training language models to follow instructions with human feedback" | |
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| "arxivId": "2303.08774", | |
| "title": "GPT-4 Technical Report" | |
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| "arxivId": "1804.07461", | |
| "title": "GLUE: A multi-task benchmark and analysis platform for natural language understanding" | |
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| "arxivId": "2201.11903", | |
| "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models" | |
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| "arxivId": "1509.01626", | |
| "title": "Character-level Convolutional Networks for Text Classification" | |
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| "arxivId": "2107.03374", | |
| "title": "Evaluating Large Language Models Trained on Code" | |
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| "arxivId": "2001.08361", | |
| "title": "Scaling Laws for Neural Language Models" | |
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| "arxivId": "2205.11916", | |
| "title": "Large Language Models are Zero-Shot Reasoners" | |
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| "arxivId": "1612.03975", | |
| "title": "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge" | |
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| "arxivId": "2110.14168", | |
| "title": "Training Verifiers to Solve Math Word Problems" | |
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| "arxivId": "2303.12712", | |
| "title": "Sparks of Artificial General Intelligence: Early experiments with GPT-4" | |
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| "arxivId": "1703.04009", | |
| "title": "Automated Hate Speech Detection and the Problem of Offensive Language" | |
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| "arxivId": "1905.00537", | |
| "title": "SuperGLUE: A stickier benchmark for general-purpose language understanding systems" | |
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| "title": "TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension" | |
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| "arxivId": "1809.09600", | |
| "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering" | |
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| "arxivId": "2206.07682", | |
| "title": "Emergent Abilities of Large Language Models" | |
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| "arxivId": "2303.18223", | |
| "title": "A Survey of Large Language Models" | |
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| "arxivId": "1905.07830", | |
| "title": "HellaSwag: Can a Machine Really Finish Your Sentence?" | |
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| "arxivId": "2202.03629", | |
| "title": "Survey of Hallucination in Natural Language Generation" | |
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| "arxivId": "2012.07805", | |
| "title": "Extracting Training Data from Large Language Models" | |
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| "arxivId": "1803.05355", | |
| "title": "FEVER: a Large-scale Dataset for Fact Extraction and VERification" | |
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| "arxivId": "2206.04615", | |
| "title": "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models" | |
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| "arxivId": "1811.00937", | |
| "title": "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge" | |
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| "arxivId": "2109.07958", | |
| "title": "TruthfulQA: Measuring How Models Mimic Human Falsehoods" | |
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| "arxivId": "1810.00278", | |
| "title": "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling" | |
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| "arxivId": "1911.11641", | |
| "title": "PIQA: Reasoning about Physical Commonsense in Natural Language" | |
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| "arxivId": "2302.04023", | |
| "title": "A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity" | |
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| "title": "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering" | |
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| "arxivId": "1709.00103", | |
| "title": "Seq2SQL: Generating structured queries from natural language using reinforcement learning" | |
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| "title": "RealToxicityPrompts: Evaluating neural toxic degeneration in language models" | |
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| "arxivId": "1811.01241", | |
| "title": "Wizard of Wikipedia: Knowledge-Powered Conversational agents" | |
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| "arxivId": "2201.07207", | |
| "title": "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents" | |
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| "arxivId": "2004.09456", | |
| "title": "StereoSet: Measuring stereotypical bias in pretrained language models" | |
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| "arxivId": "2112.04359", | |
| "title": "Ethical and social risks of harm from Language Models" | |
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| "arxivId": "1912.00741", | |
| "title": "SemEval-2017 Task 4: Sentiment Analysis in Twitter" | |
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| "title": "Predicting the type and target of offensive posts in social media" | |
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| "title": "Ex Machina: Personal Attacks Seen at Scale" | |
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| "arxivId": "1704.01074", | |
| "title": "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory" | |
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| "arxivId": "1508.00305", | |
| "title": "Compositional semantic parsing on semi-structured tables" | |
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| "arxivId": "1911.03429", | |
| "title": "ERASER: A Benchmark to Evaluate Rationalized NLP Models" | |
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| "title": "CrowS-Pairs: A challenge dataset for measuring social biases in masked language models" | |
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| "arxivId": "2101.02235", | |
| "title": "Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies" | |
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| "arxivId": "2207.05221", | |
| "title": "Language Models (Mostly) Know What They Know" | |
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| "arxivId": "2012.10289", | |
| "title": "HateXplain: A benchmark dataset for explainable hate speech detection" | |
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| "arxivId": "2305.01210", | |
| "title": "Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation" | |
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| "arxivId": "1705.09899", | |
| "title": "Understanding Abuse: A Typology of Abusive Language Detection Subtasks" | |
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| "arxivId": "1911.03891", | |
| "title": "Social bias frames: Reasoning about social and power implications of language" | |
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| "arxivId": "1909.02164", | |
| "title": "TabFact: A Large-scale Dataset for Table-based Fact Verification" | |
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| "arxivId": "1705.05414", | |
| "title": "Key-Value Retrieval Networks for Task-Oriented Dialogue" | |
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| "arxivId": "1912.00582", | |
| "title": "BLiMP: The Benchmark of Linguistic Minimal Pairs for English" | |
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| "arxivId": "2004.14974", | |
| "title": "Fact or Fiction: Verifying Scientific Claims" | |
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| "arxivId": "2305.08322", | |
| "title": "C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models" | |
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| "arxivId": "2209.11895", | |
| "title": "In-context Learning and Induction Heads" | |
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| "arxivId": "2010.05953", | |
| "title": "COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs" | |
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| "arxivId": "2009.06367", | |
| "title": "GeDi: Generative Discriminator Guided Sequence Generation" | |
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| "arxivId": "2304.06364", | |
| "title": "AGIEval: A human-centric benchmark for evaluating foundation models" | |
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| "arxivId": "2301.00234", | |
| "title": "A Survey for In-context Learning" | |
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| "arxivId": "2303.08128", | |
| "title": "ViperGPT: Visual Inference via Python Execution for Reasoning" | |
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| "arxivId": "2004.14373", | |
| "title": "ToTTo: A Controlled Table-To-Text Generation Dataset" | |
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| "arxivId": "2002.05867", | |
| "title": "Transformers as Soft Reasoners over Language" | |
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| "arxivId": "2203.15827", | |
| "title": "LinkBERT: Pretraining Language Models with Document Links" | |
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| "arxivId": "2203.14465", | |
| "title": "STaR: Bootstrapping Reasoning With Reasoning" | |
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| "arxivId": "2201.05966", | |
| "title": "UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models" | |
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| "arxivId": "2205.14334", | |
| "title": "Teaching models to express their uncertainty in words" | |
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| "arxivId": "2004.07347", | |
| "title": "HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data" | |
| }, | |
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| "arxivId": "2005.00333", | |
| "title": "XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning" | |
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| "arxivId": "2103.02582", | |
| "title": "D\u2019ya Like DAGs? A Survey on Structure Learning and Causal Discovery" | |
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| "arxivId": "2212.09597", | |
| "title": "Reasoning with Language Model Prompting: A Survey" | |
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| "arxivId": "2210.03057", | |
| "title": "Language Models are Multilingual Chain-of-Thought Reasoners" | |
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| "arxivId": "2011.00620", | |
| "title": "Social chemistry 101: Learning to reason about social and moral norms" | |
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| "arxivId": "1908.06083", | |
| "title": "Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack" | |
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| "arxivId": "2110.00976", | |
| "title": "LexGLUE: A Benchmark Dataset for Legal Language Understanding in English" | |
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| "arxivId": "2209.14610", | |
| "title": "Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning" | |
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| "arxivId": "2106.01144", | |
| "title": "Towards Emotional Support Dialog Systems" | |
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| "arxivId": "1905.06933", | |
| "title": "Dynamically Fused Graph Network for Multi-hop Reasoning" | |
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| "arxivId": "2203.01054", | |
| "title": "A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges" | |
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| "arxivId": "2305.09645", | |
| "title": "StructGPT: A General Framework for Large Language Model to Reason over Structured Data" | |
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| "arxivId": "2005.00357", | |
| "title": "Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research" | |
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| "arxivId": "1902.06977", | |
| "title": "Evaluating model calibration in classification" | |
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| "arxivId": "1908.06177", | |
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| "arxivId": "2210.02875", | |
| "title": "Binding Language Models in Symbolic Languages" | |
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| "arxivId": "2306.09212", | |
| "title": "CMMLU: Measuring massive multitask language understanding in Chinese" | |
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| "arxivId": "2305.15005", | |
| "title": "Sentiment Analysis in the Era of Large Language Models: A Reality Check" | |
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| "arxivId": "2302.09664", | |
| "title": "Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation" | |
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| "arxivId": "2301.13379", | |
| "title": "Faithful Chain-of-Thought Reasoning" | |
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| "arxivId": "2305.15771", | |
| "title": "On the Planning Abilities of Large Language Models - A Critical Investigation" | |
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| "arxivId": "2206.10498", | |
| "title": "PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change" | |
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| "arxivId": "2205.03401", | |
| "title": "The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning" | |
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| "arxivId": "2104.06039", | |
| "title": "MultiModalQA: Complex Question Answering over Text, Tables and Images" | |
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| "arxivId": "2304.04339", | |
| "title": "Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study" | |
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| "arxivId": "2012.14983", | |
| "title": "Reducing Conversational Agents\u2019 Overconfidence Through Linguistic Calibration" | |
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| "2112.08313": { | |
| "arxivId": "2112.08313", | |
| "title": "Measure and Improve Robustness in NLP Models: A Survey" | |
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| "arxivId": "2104.00369", | |
| "title": "FeTaQA: Free-form Table Question Answering" | |
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| "arxivId": "2210.17517", | |
| "title": "LILA: A Unified Benchmark for Mathematical Reasoning" | |
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| "arxivId": "2305.18153", | |
| "title": "Do large language models know what they don't know?" | |
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| "2110.06674": { | |
| "arxivId": "2110.06674", | |
| "title": "Truthful AI: Developing and governing AI that does not lie" | |
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| "arxivId": "2204.05660", | |
| "title": "NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks" | |
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| "arxivId": "2301.12867", | |
| "title": "Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity" | |
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| "arxivId": "2110.08466", | |
| "title": "On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark" | |
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| "arxivId": "2204.03021", | |
| "title": "The moral integrity corpus: A benchmark for ethical dialogue systems" | |
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| "arxivId": "2108.11830", | |
| "title": "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts" | |
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| "arxivId": "2304.14827", | |
| "title": "ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations" | |
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| "arxivId": "2305.13269", | |
| "title": "Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases" | |
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| "arxivId": "2304.10513", | |
| "title": "Why Does ChatGPT Fall Short in Providing Truthful Answers?" | |
| }, | |
| "2201.12438": { | |
| "arxivId": "2201.12438", | |
| "title": "Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey" | |
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| "2101.06223": { | |
| "arxivId": "2101.06223", | |
| "title": "LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning" | |
| }, | |
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| "arxivId": "2205.05849", | |
| "title": "e-CARE: a New Dataset for Exploring Explainable Causal Reasoning" | |
| }, | |
| "2211.07342": { | |
| "arxivId": "2211.07342", | |
| "title": "MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction" | |
| }, | |
| "2305.07375": { | |
| "arxivId": "2305.07375", | |
| "title": "Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation" | |
| }, | |
| "2202.04800": { | |
| "arxivId": "2202.04800", | |
| "title": "The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning" | |
| }, | |
| "2303.14725": { | |
| "arxivId": "2303.14725", | |
| "title": "Natural Language Reasoning, A Survey" | |
| }, | |
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| "arxivId": "2012.09157", | |
| "title": "LIREx: Augmenting Language Inference with Relevant Explanation" | |
| }, | |
| "2305.16151": { | |
| "arxivId": "2305.16151", | |
| "title": "Understanding the Capabilities of Large Language Models for Automated Planning" | |
| }, | |
| "2109.02738": { | |
| "arxivId": "2109.02738", | |
| "title": "Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica" | |
| }, | |
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| "arxivId": "2305.16837", | |
| "title": "ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks" | |
| }, | |
| "2301.04449": { | |
| "arxivId": "2301.04449", | |
| "title": "Diving Deep into Modes of Fact Hallucinations in Dialogue Systems" | |
| }, | |
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| "arxivId": "2208.05358", | |
| "title": "CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning" | |
| }, | |
| "2212.10923": { | |
| "arxivId": "2212.10923", | |
| "title": "Language Models as Inductive Reasoners" | |
| }, | |
| "2209.08207": { | |
| "arxivId": "2209.08207", | |
| "title": "APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations" | |
| }, | |
| "2205.11097": { | |
| "arxivId": "2205.11097", | |
| "title": "A Fine-grained Interpretability Evaluation Benchmark for Neural NLP" | |
| }, | |
| "2205.10228": { | |
| "arxivId": "2205.10228", | |
| "title": "You Don\u2019t Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers\u2019 Private Personas" | |
| }, | |
| "2010.12896": { | |
| "arxivId": "2010.12896", | |
| "title": "Abduction and Argumentation for Explainable Machine Learning: A Position Survey" | |
| }, | |
| "2304.09842": { | |
| "arxivId": "2304.09842", | |
| "title": "Chameleon: Plug-and-play compositional reasoning with large language models" | |
| }, | |
| "2303.15621": { | |
| "arxivId": "2303.15621", | |
| "title": "ChatGPT as a Factual Inconsistency Evaluator for Text Summarization" | |
| }, | |
| "2110.07871": { | |
| "arxivId": "2110.07871", | |
| "title": "Socially aware bias measurements for Hindi language representations" | |
| }, | |
| "1307.5336": { | |
| "arxivId": "1307.5336", | |
| "title": "Good debt or bad debt: Detecting semantic orientations in economic texts" | |
| }, | |
| "2303.08896": { | |
| "arxivId": "2303.08896", | |
| "title": "SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models" | |
| }, | |
| "2005.00661": { | |
| "arxivId": "2005.00661", | |
| "title": "On faithfulness and factuality in abstractive summarization" | |
| }, | |
| "2302.07842": { | |
| "arxivId": "2302.07842", | |
| "title": "Augmented Language Models: a Survey" | |
| }, | |
| "2106.15772": { | |
| "arxivId": "2106.15772", | |
| "title": "A diverse corpus for evaluating and developing English math word problem solvers" | |
| }, | |
| "2305.14251": { | |
| "arxivId": "2305.14251", | |
| "title": "FactScore: Fine-grained atomic evaluation of factual precision in long form text generation" | |
| }, | |
| "2112.09332": { | |
| "arxivId": "2112.09332", | |
| "title": "WebGPT: Browser-assisted question-answering with human feedback" | |
| }, | |
| "2304.06912": { | |
| "arxivId": "2304.06912", | |
| "title": "How well do SOTA legal reasoning models support abductive reasoning?" | |
| }, | |
| "1910.14599": { | |
| "arxivId": "1910.14599", | |
| "title": "Adversarial NLI: A new benchmark for natural language understanding" | |
| }, | |
| "2303.13375": { | |
| "arxivId": "2303.13375", | |
| "title": "Capabilities of GPT-4 on medical challenge problems" | |
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| "title": "Discovering language model behaviors with model-written evaluations" | |
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| "title": "Evaluating language-model agents on realistic autonomous tasks" | |
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| "title": "Performance of ChatGPT on USMLE: Unlocking the potential of large language models for AI-assisted medical education" | |
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| "title": "Exploring the robustness of large language models for solving programming problems" | |
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| "title": "ALFRED: A benchmark for interpreting grounded instructions for everyday tasks" | |
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| "title": "Large language models encode clinical knowledge" | |
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| "arxivId": "2305.09617", | |
| "title": "Towards Expert-Level Medical Question Answering with Large Language Models" | |
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| "title": "Impact of News on the Commodity Market: Dataset and Results" | |
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| "title": "Beyond classification: Financial reasoning in state-of-the-art language models" | |
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| "title": "RestGPT: Connecting large language models with real-world applications via RESTful APIs" | |
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| "title": "BEHAVIOR: Benchmark for everyday household activities in virtual, interactive, and ecological environments" | |
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| "title": "A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models" | |
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| "title": "Recitation-augmented language models" | |
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| "title": "Evaluating the factual consistency of large language models through news summarization" | |
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| "title": "ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases" | |
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| "title": "Do multi-hop question answering systems know how to answer the single-hop sub-questions?" | |
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| "title": "Transformer-based language models for software vulnerability detection" | |
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| "title": "LaMDA: Language models for dialog applications" | |
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| "title": "On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective" | |
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| "title": "Plan-and-Solve prompting: Improving zero-shot chain-of-thought reasoning by large language models" | |
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| "title": "Toxicity detection with generative prompt-based inference" | |
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| "title": "Self-Instruct: Aligning Language Models with Self-Generated Instructions" | |
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| "title": "Jailbroken: How Does LLM Safety Training Fail?" | |
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| "title": "Constructing datasets for multi-hop reading comprehension across documents" | |
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| "title": "On the Tool Manipulation Capability of Open-source Large Language Models" | |
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| "title": "Can neural networks understand monotonicity reasoning?" | |
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| "title": "HELP: A dataset for identifying shortcomings of neural models in monotonicity reasoning" | |
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| "title": "WebShop: Towards scalable real-world web interaction with grounded language agents" | |
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| "title": "ReAct: Synergizing Reasoning and Acting in Language Models" | |
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| "title": "How well do Large Language Models perform in Arithmetic tasks?" | |
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| "title": "SemEval-2019 Task 6: Identifying and categorizing offensive language in social media (OffensEval)" | |
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| "title": "Measuring massive multitask Chinese understanding" | |
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| "arxivId": "2307.13854", | |
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| "title": "Can You Put it All Together: Evaluating Conversational Agents\u2019 Ability to Blend Skills" | |
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| "title": "\u201cI\u2019m sorry to hear that\u201d: Finding New Biases in Language Models with a Holistic Descriptor Dataset" | |
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| "title": "DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications" | |
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| "title": "\u201cGoing on a vacation\u201d takes longer than \u201cGoing for a walk\u201d: A Study of Temporal Commonsense Understanding" | |
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| "title": "FinBERT: Financial sentiment analysis with pre-trained language models" | |
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| "title": "Have LLMs advanced enough? A challenging problem solving benchmark for large language models" | |
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| "title": "Can GPT-3 Perform Statutory Reasoning?" | |
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| "title": "Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings" | |
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| "title": "A large annotated corpus for learning natural language inference" | |
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| "arxivId": "2305.17126", | |
| "title": "Large Language Models as Tool Makers" | |
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| "arxivId": "1608.07187", | |
| "title": "Semantics derived automatically from language corpora contain human-like biases" | |
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| "title": "CLIFF: contrastive learning for improving faithfulness and factuality in abstractive summarization" | |
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| "title": "Toward gender-inclusive coreference resolution" | |
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| "title": "Is power-seeking AI an existential risk?" | |
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| "arxivId": "2307.03109", | |
| "title": "A Survey on Evaluation of Large Language Models" | |
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| "arxivId": "2307.09009", | |
| "title": "How is ChatGPT's behavior changing over time?" | |
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| "arxivId": "2211.12588", | |
| "title": "Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks" | |
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| "arxivId": "2307.08678", | |
| "title": "Do models explain themselves? counterfactual simulatability of natural language explanations" | |
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| "title": "Exploring the use of large language models for reference-free text quality evaluation: A preliminary empirical study" | |
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| "title": "ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering" | |
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| "title": "Evaluating hallucinations in chinese large language models" | |
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| "title": "Factool: Factuality detection in generative AI - A tool augmented framework for multi-task and multi-domain scenarios" | |
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| "title": "PaLM: Scaling language modeling with pathways" | |
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| "title": "Deep reinforcement learning from human preferences" | |
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| "title": "Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge" | |
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| "title": "Evaluating language models for mathematics through interactions" | |
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| "arxivId": "1809.05053", | |
| "title": "XNLI: evaluating cross-lingual sentence representations" | |
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| "arxivId": "2305.13198", | |
| "title": "Multilingual holistic bias: Extending descriptors and patterns to unveil demographic biases in languages at scale" | |
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| "arxivId": "2305.12199", | |
| "title": "VNHSGE: vietnamese high school graduation examination dataset for large language models" | |
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| "arxivId": "1905.12516", | |
| "title": "Racial bias in hate speech and abusive language detection datasets" | |
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| "arxivId": "2306.01248", | |
| "title": "How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?" | |
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| "arxivId": "2304.05335", | |
| "title": "Toxicity in ChatGPT: Analyzing persona-assigned language models" | |
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| "arxivId": "1908.09369", | |
| "title": "On measuring and mitigating biased inferences of word embeddings" | |
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| "arxivId": "2108.03362", | |
| "title": "On measures of biases and harms in NLP" | |
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| "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" | |
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| "arxivId": "2101.11718", | |
| "title": "BOLD: dataset and metrics for measuring biases in open-ended language generation" | |
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| "arxivId": "1902.00098", | |
| "title": "The second conversational intelligence challenge (convai2)" | |
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| "arxivId": "2305.14387", | |
| "title": "AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback" | |
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| "arxivId": "2005.03754", | |
| "title": "FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization" | |
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| "arxivId": "2204.10757", | |
| "title": "Faithdial: A faithful benchmark for information-seeking dialogue" | |
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| "arxivId": "2105.00071", | |
| "title": "Evaluating attribution in dialogue systems: The BEGIN benchmark" | |
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| "arxivId": "2109.05322", | |
| "title": "Latent hatred: A benchmark for understanding implicit hate speech" | |
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| "arxivId": "2012.15738", | |
| "title": "Moral stories: Situated reasoning about norms, intents, actions, and their consequences" | |
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| "arxivId": "2007.12626", | |
| "title": "Summeval: Re-evaluating summarization evaluation" | |
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| "arxivId": "2112.08542", | |
| "title": "QAFactEval: Improved QA-based factual consistency evaluation for summarization" | |
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| "arxivId": "2306.09983", | |
| "title": "Evaluating superhuman models with consistency checks" | |
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| "title": "GPTScore: Evaluate as you desire" | |
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| "title": "Understanding social reasoning in language models with language models" | |
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| "arxivId": "2211.10435", | |
| "title": "PAL: program-aided language models" | |
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| "arxivId": "1911.03642", | |
| "title": "Towards understanding gender bias in relation extraction" | |
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| "arxivId": "2305.11171", | |
| "title": "TrueTeacher: Learning factual consistency evaluation with large language models" | |
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| "arxivId": "1905.13322", | |
| "title": "Assessing the factual accuracy of generated text" | |
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| "title": "Evaluating factuality in generation with dependency-level entailment" | |
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| "arxivId": "2110.08222", | |
| "title": "DialFact: A benchmark for fact-checking in dialogue" | |
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| "arxivId": "2209.00840", | |
| "title": "FOLIO: natural language reasoning with first-order logic" | |
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| "arxivId": "2305.11554", | |
| "title": "ToolkenGPT: Augmenting frozen language models with massive tools via tool embeddings" | |
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| "arxivId": "2203.09509", | |
| "title": "ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection" | |
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| "arxivId": "2005.12423", | |
| "title": "Racism is a virus: anti-asian hate and counterspeech in social media during the COVID-19 crisis" | |
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| "arxivId": "2008.02275", | |
| "title": "Aligning AI With Shared Human Values" | |
| }, | |
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| "arxivId": "2009.03300", | |
| "title": "Measuring Massive Multitask Language Understanding" | |
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| "arxivId": "2103.03874", | |
| "title": "Measuring Mathematical Problem Solving With the MATH Dataset" | |
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| "arxivId": "2005.05257", | |
| "title": "A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering" | |
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| "arxivId": "2301.09211", | |
| "title": "An empirical study of metrics to measure representational harms in pre-trained language models" | |
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| "arxivId": "2308.00675", | |
| "title": "Tool documentation enables zero-shot tool-usage with large language models" | |
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| "arxivId": "2010.04529", | |
| "title": "What have we achieved on text summarization?" | |
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| "arxivId": "2302.07736", | |
| "title": "Is ChatGPT better than human annotators? Potential and limitations of ChatGPT in explaining implicit hate speech" | |
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| "arxivId": "2207.05608", | |
| "title": "Inner Monologue: Embodied Reasoning through Planning with Language Models" | |
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| "arxivId": "2306.11507", | |
| "title": "TrustGPT: A benchmark for trustworthy and responsible large language models" | |
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| "arxivId": "2306.16244", | |
| "title": "CBBQ: A chinese bias benchmark dataset curated with human-AI collaboration for large language models" | |
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| "arxivId": "2005.00813", | |
| "title": "Social biases in NLP models as barriers for persons with disabilities" | |
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| "arxivId": "2204.01691", | |
| "title": "Do as I can, not as I say: Grounding language in robotic affordances" | |
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| "arxivId": "2306.01200", | |
| "title": "Multi-dimensional evaluation of text summarization with in-context learning" | |
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| "arxivId": "2303.07610", | |
| "title": "Exploring ChatGPT's ability to rank content: A preliminary study on consistency with human preferences" | |
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| "arxivId": "1909.06146", | |
| "title": "PubMedQA: A Dataset for Biomedical Research Question Answering" | |
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| "arxivId": "2304.09667", | |
| "title": "GeneGPT: Augmenting large language models with domain tools for improved access to biomedical information" | |
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| "arxivId": "2210.01478", | |
| "title": "When to make exceptions: Exploring language models as accounts of human moral judgment" | |
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| "title": "TaxiNLI: Taking a ride up the NLU hill" | |
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| "arxivId": "2005.00700", | |
| "title": "UnifiedQA: Crossing format boundaries with a single QA system" | |
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| "title": "QASC: A dataset for question answering via sentence composition" | |
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| "arxivId": "2104.14337", | |
| "title": "Dynabench: Rethinking benchmarking in NLP" | |
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| "arxivId": "2205.12688", | |
| "title": "ProsocialDialog: A prosocial backbone for conversational agents" | |
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| "title": "Examining gender and race bias in two hundred sentiment analysis systems" | |
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| "title": "Reformer: The efficient transformer" | |
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| "arxivId": "1712.07040", | |
| "title": "The NarrativeQA reading comprehension challenge" | |
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| "title": "Large language models are state-of-the-art evaluators of translation quality" | |
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| "title": "Evaluating the factual consistency of abstractive text summarization" | |
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| "arxivId": "2011.01575", | |
| "title": "AraWEAT: Multidimensional analysis of biases in Arabic word embeddings" | |
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| "arxivId": "1705.04146", | |
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| "title": "Natural language inference in context - investigating contextual reasoning over long texts" | |
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| "arxivId": "1910.10486", | |
| "title": "Does gender matter? Towards fairness in dialogue systems" | |
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| "arxivId": "2007.08124", | |
| "title": "LogiQA: A challenge dataset for machine reading comprehension with logical reasoning" | |
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| "title": "Training socially aligned language models in simulated human society" | |
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| "arxivId": "2308.05374", | |
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| "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach" | |
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| "title": "Multilingual denoising pre-training for neural machine translation" | |
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| "arxivId": "2304.10619", | |
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| "arxivId": "2304.08244", | |
| "title": "API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs" | |
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| "arxivId": "2010.02428", | |
| "title": "UNQOVERing Stereotypical Biases via Underspecified Questions" | |
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| "arxivId": "1706.03762", | |
| "title": "Attention is All you Need" | |
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| "title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks" | |
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| "arxivId": "2111.01243", | |
| "title": "Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey" | |
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| "arxivId": "2303.11366", | |
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| "arxivId": "2308.11432", | |
| "title": "A Survey on Large Language Model based Autonomous Agents" | |
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| "arxivId": "1907.09190", | |
| "title": "ELI5: Long Form Question Answering" | |
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| "arxivId": "1908.05739", | |
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| "title": "Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting" | |
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| "arxivId": "2203.11147", | |
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| "title": "Language to Rewards for Robotic Skill Synthesis" | |
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| "arxivId": "2307.01928", | |
| "title": "Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners" | |
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| "arxivId": "2104.08661", | |
| "title": "Explaining Answers with Entailment Trees" | |
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| "arxivId": "2208.03274", | |
| "title": "A Holistic Approach to Undesired Content Detection in the Real World" | |
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| "arxivId": "2307.06135", | |
| "title": "SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Task Planning" | |
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| "arxivId": "2310.19736", | |
| "title": "Evaluating Large Language Models: A Comprehensive Survey" | |
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| "arxivId": "2308.15363", | |
| "title": "Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation" | |
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| "arxivId": "2305.07609", | |
| "title": "Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation" | |
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| "arxivId": "2010.12773", | |
| "title": "Structure-Grounded Pretraining for Text-to-SQL" | |
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| "arxivId": "2011.03088", | |
| "title": "HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification" | |
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| "2305.16653": { | |
| "arxivId": "2305.16653", | |
| "title": "AdaPlanner: Adaptive Planning from Feedback with Language Models" | |
| }, | |
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| "arxivId": "2306.05443", | |
| "title": "PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance" | |
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| "arxivId": "2306.03901", | |
| "title": "ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory" | |
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| "arxivId": "2308.00436", | |
| "title": "SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning" | |
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| "arxivId": "2305.16986", | |
| "title": "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models" | |
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| "arxivId": "2311.10723", | |
| "title": "Large Language Models in Finance: A Survey" | |
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| "arxivId": "2211.14228", | |
| "title": "GPT-3-Driven Pedagogical Agents to Train Children\u2019s Curious Question-Asking Skills" | |
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| "arxivId": "2107.00285", | |
| "title": "iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis" | |
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| "arxivId": "2108.04634", | |
| "title": "Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews" | |
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| "arxivId": "2311.07397", | |
| "title": "An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation" | |
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| "arxivId": "2307.09705", | |
| "title": "CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility" | |
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| "arxivId": "2310.17389", | |
| "title": "ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation" | |
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| "arxivId": "2404.00971", | |
| "title": "Exploring and Evaluating Hallucinations in LLM-Powered Code Generation" | |
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| "arxivId": "2402.05044", | |
| "title": "SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models" | |
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| "arxivId": "2312.11562", | |
| "title": "A Survey of Reasoning with Foundation Models" | |
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| "arxivId": "2402.00888", | |
| "title": "Security and Privacy Challenges of Large Language Models: A Survey" | |
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| "title": "Trapping LLM Hallucinations Using Tagged Context Prompts" | |
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| "arxivId": "2309.00779", | |
| "title": "Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties" | |
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| "arxivId": "2402.06782", | |
| "title": "Debating with More Persuasive LLMs Leads to More Truthful Answers" | |
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| "arxivId": "2110.03895", | |
| "title": "ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments" | |
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| "arxivId": "2401.07103", | |
| "title": "Leveraging Large Language Models for NLG Evaluation: A Survey" | |
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| "arxivId": "2403.05156", | |
| "title": "On Protecting the Data Privacy of Large Language Models (LLMs): A Survey" | |
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| "arxivId": "2401.07339", | |
| "title": "CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges" | |
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| "arxivId": "2310.06498", | |
| "title": "A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection" | |
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| "arxivId": "2403.02691", | |
| "title": "InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents" | |
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| "arxivId": "2403.13031", | |
| "title": "RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content" | |
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| "arxivId": "2209.11830", | |
| "title": "Multiple-Choice Question Generation: Towards an Automated Assessment Framework" | |
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| "arxivId": "0810.1922", | |
| "title": "Look-Ahead Benchmark Bias in Portfolio Performance Evaluation" | |
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| "arxivId": "2402.13249", | |
| "title": "TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization" | |
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| "2402.02315": { | |
| "arxivId": "2402.02315", | |
| "title": "A Survey of Large Language Models in Finance (FinLLMs)" | |
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| "2402.09267": { | |
| "arxivId": "2402.09267", | |
| "title": "Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation" | |
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| "arxivId": "2304.13148", | |
| "title": "Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection" | |
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| "arxivId": "2203.12186", | |
| "title": "AbductionRules: Training Transformers to Explain Unexpected Inputs" | |
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| "arxivId": "2312.02010", | |
| "title": "Towards Learning a Generalist Model for Embodied Navigation" | |
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| "2402.10412": { | |
| "arxivId": "2402.10412", | |
| "title": "Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting" | |
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| "2404.05993": { | |
| "arxivId": "2404.05993", | |
| "title": "AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts" | |
| }, | |
| "2402.11443": { | |
| "arxivId": "2402.11443", | |
| "title": "Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation" | |
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| "2401.00991": { | |
| "arxivId": "2401.00991", | |
| "title": "A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models" | |
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| "2302.13971": { | |
| "arxivId": "2302.13971", | |
| "title": "LLaMA: Open and Efficient Foundation Language Models" | |
| }, | |
| "1904.09675": { | |
| "arxivId": "1904.09675", | |
| "title": "BERTScore: Evaluating Text Generation with BERT" | |
| }, | |
| "2005.11401": { | |
| "arxivId": "2005.11401", | |
| "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" | |
| }, | |
| "2104.08691": { | |
| "arxivId": "2104.08691", | |
| "title": "The Power of Scale for Parameter-Efficient Prompt Tuning" | |
| }, | |
| "2210.11416": { | |
| "arxivId": "2210.11416", | |
| "title": "Scaling Instruction-Finetuned Language Models" | |
| }, | |
| "2310.03744": { | |
| "arxivId": "2310.03744", | |
| "title": "Improved Baselines with Visual Instruction Tuning" | |
| }, | |
| "2305.06500": { | |
| "arxivId": "2305.06500", | |
| "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning" | |
| }, | |
| "2308.12950": { | |
| "arxivId": "2308.12950", | |
| "title": "Code Llama: Open Foundation Models for Code" | |
| }, | |
| "2310.06825": { | |
| "arxivId": "2310.06825", | |
| "title": "Mistral 7B" | |
| }, | |
| "1704.04683": { | |
| "arxivId": "1704.04683", | |
| "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations" | |
| }, | |
| "1905.10044": { | |
| "arxivId": "1905.10044", | |
| "title": "BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions" | |
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| "2203.07814": { | |
| "arxivId": "2203.07814", | |
| "title": "Competition-level code generation with AlphaCode" | |
| }, | |
| "2307.03172": { | |
| "arxivId": "2307.03172", | |
| "title": "Lost in the Middle: How Language Models Use Long Contexts" | |
| }, | |
| "2209.09513": { | |
| "arxivId": "2209.09513", | |
| "title": "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering" | |
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| "2304.14178": { | |
| "arxivId": "2304.14178", | |
| "title": "mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality" | |
| }, | |
| "2312.10997": { | |
| "arxivId": "2312.10997", | |
| "title": "Retrieval-Augmented Generation for Large Language Models: A Survey" | |
| }, | |
| "2106.11520": { | |
| "arxivId": "2106.11520", | |
| "title": "BARTScore: Evaluating Generated Text as Text Generation" | |
| }, | |
| "2308.12966": { | |
| "arxivId": "2308.12966", | |
| "title": "Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities" | |
| }, | |
| "2403.05530": { | |
| "arxivId": "2403.05530", | |
| "title": "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context" | |
| }, | |
| "2302.06476": { | |
| "arxivId": "2302.06476", | |
| "title": "Is ChatGPT a General-Purpose Natural Language Processing Task Solver?" | |
| }, | |
| "2003.05002": { | |
| "arxivId": "2003.05002", | |
| "title": "TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages" | |
| }, | |
| "1911.12237": { | |
| "arxivId": "1911.12237", | |
| "title": "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" | |
| }, | |
| "2307.06281": { | |
| "arxivId": "2307.06281", | |
| "title": "MMBench: Is Your Multi-modal Model an All-around Player?" | |
| }, | |
| "2305.06161": { | |
| "arxivId": "2305.06161", | |
| "title": "StarCoder: may the source be with you!" | |
| }, | |
| "1803.06643": { | |
| "arxivId": "1803.06643", | |
| "title": "The Web as a Knowledge-Base for Answering Complex Questions" | |
| }, | |
| "2202.03286": { | |
| "arxivId": "2202.03286", | |
| "title": "Red Teaming Language Models with Language Models" | |
| }, | |
| "2105.09938": { | |
| "arxivId": "2105.09938", | |
| "title": "Measuring Coding Challenge Competence With APPS" | |
| }, | |
| "2007.00398": { | |
| "arxivId": "2007.00398", | |
| "title": "DocVQA: A Dataset for VQA on Document Images" | |
| }, | |
| "2404.14219": { | |
| "arxivId": "2404.14219", | |
| "title": "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone" | |
| }, | |
| "2305.01937": { | |
| "arxivId": "2305.01937", | |
| "title": "Can Large Language Models Be an Alternative to Human Evaluations?" | |
| }, | |
| "2302.10724": { | |
| "arxivId": "2302.10724", | |
| "title": "ChatGPT: Jack of all trades, master of none" | |
| }, | |
| "2305.17926": { | |
| "arxivId": "2305.17926", | |
| "title": "Large Language Models are not Fair Evaluators" | |
| }, | |
| "2311.16502": { | |
| "arxivId": "2311.16502", | |
| "title": "MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI" | |
| }, | |
| "2307.05782": { | |
| "arxivId": "2307.05782", | |
| "title": "Large Language Models" | |
| }, | |
| "2308.02490": { | |
| "arxivId": "2308.02490", | |
| "title": "MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities" | |
| }, | |
| "2301.13848": { | |
| "arxivId": "2301.13848", | |
| "title": "Benchmarking Large Language Models for News Summarization" | |
| }, | |
| "1702.01806": { | |
| "arxivId": "1702.01806", | |
| "title": "Beam Search Strategies for Neural Machine Translation" | |
| }, | |
| "2311.12793": { | |
| "arxivId": "2311.12793", | |
| "title": "ShareGPT4V: Improving Large Multi-Modal Models with Better Captions" | |
| }, | |
| "2307.16125": { | |
| "arxivId": "2307.16125", | |
| "title": "SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension" | |
| }, | |
| "2203.10244": { | |
| "arxivId": "2203.10244", | |
| "title": "ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning" | |
| }, | |
| "1603.07396": { | |
| "arxivId": "1603.07396", | |
| "title": "A Diagram is Worth a Dozen Images" | |
| }, | |
| "2407.10671": { | |
| "arxivId": "2407.10671", | |
| "title": "Qwen2 Technical Report" | |
| }, | |
| "2311.16867": { | |
| "arxivId": "2311.16867", | |
| "title": "The Falcon Series of Open Language Models" | |
| }, | |
| "2310.02255": { | |
| "arxivId": "2310.02255", | |
| "title": "MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts" | |
| }, | |
| "2309.05922": { | |
| "arxivId": "2309.05922", | |
| "title": "A Survey of Hallucination in Large Foundation Models" | |
| }, | |
| "2403.08295": { | |
| "arxivId": "2403.08295", | |
| "title": "Gemma: Open Models Based on Gemini Research and Technology" | |
| }, | |
| "2303.10420": { | |
| "arxivId": "2303.10420", | |
| "title": "A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models" | |
| }, | |
| "2304.05613": { | |
| "arxivId": "2304.05613", | |
| "title": "ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning" | |
| }, | |
| "2012.15613": { | |
| "arxivId": "2012.15613", | |
| "title": "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" | |
| }, | |
| "2305.10425": { | |
| "arxivId": "2305.10425", | |
| "title": "SLiC-HF: Sequence Likelihood Calibration with Human Feedback" | |
| }, | |
| "2303.12528": { | |
| "arxivId": "2303.12528", | |
| "title": "MEGA: Multilingual Evaluation of Generative AI" | |
| }, | |
| "2310.11324": { | |
| "arxivId": "2310.11324", | |
| "title": "Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting" | |
| }, | |
| "2212.10450": { | |
| "arxivId": "2212.10450", | |
| "title": "Is GPT-3 a Good Data Annotator?" | |
| }, | |
| "2310.06770": { | |
| "arxivId": "2310.06770", | |
| "title": "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" | |
| }, | |
| "2308.07107": { | |
| "arxivId": "2308.07107", | |
| "title": "Large Language Models for Information Retrieval: A Survey" | |
| }, | |
| "2309.15112": { | |
| "arxivId": "2309.15112", | |
| "title": "InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition" | |
| }, | |
| "2305.18486": { | |
| "arxivId": "2305.18486", | |
| "title": "A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets" | |
| }, | |
| "2306.14565": { | |
| "arxivId": "2306.14565", | |
| "title": "Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning" | |
| }, | |
| "2108.11601": { | |
| "arxivId": "2108.11601", | |
| "title": "Retrieval Augmented Code Generation and Summarization" | |
| }, | |
| "2307.13702": { | |
| "arxivId": "2307.13702", | |
| "title": "Measuring Faithfulness in Chain-of-Thought Reasoning" | |
| }, | |
| "2403.13787": { | |
| "arxivId": "2403.13787", | |
| "title": "RewardBench: Evaluating Reward Models for Language Modeling" | |
| }, | |
| "2310.18018": { | |
| "arxivId": "2310.18018", | |
| "title": "NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark" | |
| }, | |
| "2311.01964": { | |
| "arxivId": "2311.01964", | |
| "title": "Don't Make Your LLM an Evaluation Benchmark Cheater" | |
| }, | |
| "2304.02554": { | |
| "arxivId": "2304.02554", | |
| "title": "Human-like Summarization Evaluation with ChatGPT" | |
| }, | |
| "2402.03927": { | |
| "arxivId": "2402.03927", | |
| "title": "Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs" | |
| }, | |
| "2310.17623": { | |
| "arxivId": "2310.17623", | |
| "title": "Proving Test Set Contamination in Black Box Language Models" | |
| }, | |
| "2403.20330": { | |
| "arxivId": "2403.20330", | |
| "title": "Are We on the Right Way for Evaluating Large Vision-Language Models?" | |
| }, | |
| "2310.14566": { | |
| "arxivId": "2310.14566", | |
| "title": "Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models" | |
| }, | |
| "2405.01535": { | |
| "arxivId": "2405.01535", | |
| "title": "Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models" | |
| }, | |
| "2402.13178": { | |
| "arxivId": "2402.13178", | |
| "title": "Benchmarking Retrieval-Augmented Generation for Medicine" | |
| }, | |
| "2312.16337": { | |
| "arxivId": "2312.16337", | |
| "title": "Task Contamination: Language Models May Not Be Few-Shot Anymore" | |
| }, | |
| "2308.08493": { | |
| "arxivId": "2308.08493", | |
| "title": "Time Travel in LLMs: Tracing Data Contamination in Large Language Models" | |
| }, | |
| "2308.12488": { | |
| "arxivId": "2308.12488", | |
| "title": "GPTEval: A Survey on Assessments of ChatGPT and GPT-4" | |
| }, | |
| "2211.08073": { | |
| "arxivId": "2211.08073", | |
| "title": "GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective" | |
| }, | |
| "2406.01574": { | |
| "arxivId": "2406.01574", | |
| "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" | |
| }, | |
| "2404.12390": { | |
| "arxivId": "2404.12390", | |
| "title": "BLINK: Multimodal Large Language Models Can See but Not Perceive" | |
| }, | |
| "2405.00332": { | |
| "arxivId": "2405.00332", | |
| "title": "A Careful Examination of Large Language Model Performance on Grade School Arithmetic" | |
| }, | |
| "2404.01318": { | |
| "arxivId": "2404.01318", | |
| "title": "JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models" | |
| }, | |
| "2401.06855": { | |
| "arxivId": "2401.06855", | |
| "title": "Fine-grained Hallucination Detection and Editing for Language Models" | |
| }, | |
| "2211.02011": { | |
| "arxivId": "2211.02011", | |
| "title": "Inverse scaling can become U-shaped" | |
| }, | |
| "2305.14976": { | |
| "arxivId": "2305.14976", | |
| "title": "GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP" | |
| }, | |
| "2307.02503": { | |
| "arxivId": "2307.02503", | |
| "title": "Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review" | |
| }, | |
| "2305.13091": { | |
| "arxivId": "2305.13091", | |
| "title": "Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization" | |
| }, | |
| "2010.03636": { | |
| "arxivId": "2010.03636", | |
| "title": "MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics" | |
| }, | |
| "2404.18796": { | |
| "arxivId": "2404.18796", | |
| "title": "Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models" | |
| }, | |
| "2404.19737": { | |
| "arxivId": "2404.19737", | |
| "title": "Better & Faster Large Language Models via Multi-token Prediction" | |
| }, | |
| "2311.08377": { | |
| "arxivId": "2311.08377", | |
| "title": "Learning to Filter Context for Retrieval-Augmented Generation" | |
| }, | |
| "2305.11116": { | |
| "arxivId": "2305.11116", | |
| "title": "LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation" | |
| }, | |
| "2202.07654": { | |
| "arxivId": "2202.07654", | |
| "title": "Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation" | |
| }, | |
| "1805.04836": { | |
| "arxivId": "1805.04836", | |
| "title": "Building Language Models for Text with Named Entities" | |
| }, | |
| "2406.06608": { | |
| "arxivId": "2406.06608", | |
| "title": "The Prompt Report: A Systematic Survey of Prompting Techniques" | |
| }, | |
| "2309.07462": { | |
| "arxivId": "2309.07462", | |
| "title": "Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?" | |
| }, | |
| "2303.03004": { | |
| "arxivId": "2303.03004", | |
| "title": "xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" | |
| }, | |
| "2112.11670": { | |
| "arxivId": "2112.11670", | |
| "title": "Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization" | |
| }, | |
| "2309.13633": { | |
| "arxivId": "2309.13633", | |
| "title": "EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria" | |
| }, | |
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| "arxivId": "2402.18667", | |
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| "title": "A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks" | |
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| "title": "A Survey on Mixture of Experts" | |
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| "title": "MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures" | |
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| "title": "Lessons from the Trenches on Reproducible Evaluation of Language Models" | |
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| "title": "A Thorough Examination of Decoding Methods in the Era of LLMs" | |
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| "title": "Large Language Models are Inconsistent and Biased Evaluators" | |
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| "title": "Evaluating the Values of Sources in Transfer Learning" | |
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| "arxivId": "2304.13620", | |
| "title": "ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries" | |
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| "title": "An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Models are Task-specific Classifiers" | |
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| "arxivId": "2211.09110", | |
| "title": "Holistic Evaluation of Language Models" | |
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| "title": "QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension" | |
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| "title": "Deep Learning with Differential Privacy" | |
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| "title": "Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering" | |
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| "title": "Word embeddings quantify 100 years of gender and ethnic stereotypes" | |
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| "arxivId": "2307.15043", | |
| "title": "Universal and Transferable Adversarial Attacks on Aligned Language Models" | |
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| "title": "How Much Knowledge Can You Pack into the Parameters of a Language Model?" | |
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| "title": "Scalable Private Learning with PATE" | |
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| "title": "Learning the Difference that Makes a Difference with Counterfactually-Augmented Data" | |
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| "title": "Extracting Training Data from Diffusion Models" | |
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| "title": "DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature" | |
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| "title": "Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting" | |
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| "title": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned" | |
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| "arxivId": "2310.03693", | |
| "title": "Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!" | |
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| "title": "Measuring Attribution in Natural Language Generation Models" | |
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| "arxivId": "2305.14552", | |
| "title": "Sources of Hallucination by Large Language Models on Inference Tasks" | |
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| "arxivId": "2305.14739", | |
| "title": "Trusting Your Evidence: Hallucinate Less with Context-aware Decoding" | |
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| "title": "NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails" | |
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| "title": "The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations" | |
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| "arxivId": "2112.09238", | |
| "title": "Benchmarking Differentially Private Synthetic Data Generation Algorithms" | |
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| "title": "Differentially Private Query Release Through Adaptive Projection" | |
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| "arxivId": "2102.13004", | |
| "title": "Towards Unbiased and Accurate Deferral to Multiple Experts" | |
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| "arxivId": "2403.14403", | |
| "title": "Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity" | |
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| "arxivId": "2311.09476", | |
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| "arxivId": "2312.04724", | |
| "title": "Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models" | |
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| "arxivId": "2212.11261", | |
| "title": "Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias" | |
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| "title": "Rethinking search" | |
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| "arxivId": "2312.03689", | |
| "title": "Evaluating and Mitigating Discrimination in Language Model Decisions" | |
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| "arxivId": "2310.05344", | |
| "title": "SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF" | |
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| "arxivId": "2202.08821", | |
| "title": "Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness" | |
| }, | |
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| "arxivId": "2310.18491", | |
| "title": "Publicly Detectable Watermarking for Language Models" | |
| }, | |
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| "arxivId": "2210.07543", | |
| "title": "Watermarking Pre-trained Language Models with Backdooring" | |
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| "arxivId": "2310.11689", | |
| "title": "Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs" | |
| }, | |
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| "arxivId": "2311.18799", | |
| "title": "X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning" | |
| }, | |
| "2310.18168": { | |
| "arxivId": "2310.18168", | |
| "title": "Personas as a Way to Model Truthfulness in Language Models" | |
| }, | |
| "2404.00610": { | |
| "arxivId": "2404.00610", | |
| "title": "RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation" | |
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| "arxivId": "2311.09677", | |
| "title": "R-Tuning: Instructing Large Language Models to Say \u2018I Don\u2019t Know\u2019" | |
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| "arxivId": "2404.10198", | |
| "title": "ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence" | |
| }, | |
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| "arxivId": "2402.14875", | |
| "title": "What's in a Name? Auditing Large Language Models for Race and Gender Bias" | |
| }, | |
| "2402.04105": { | |
| "arxivId": "2402.04105", | |
| "title": "Measuring Implicit Bias in Explicitly Unbiased Large Language Models" | |
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| "arxivId": "2203.07860", | |
| "title": "Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost" | |
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| "arxivId": "2202.08919", | |
| "title": "Debiaser Beware: Pitfalls of Centering Regularized Transport Maps" | |
| }, | |
| "2312.14183": { | |
| "arxivId": "2312.14183", | |
| "title": "On Early Detection of Hallucinations in Factual Question Answering" | |
| }, | |
| "2305.13514": { | |
| "arxivId": "2305.13514", | |
| "title": "Small Language Models Improve Giants by Rewriting Their Outputs" | |
| }, | |
| "2204.04440": { | |
| "arxivId": "2204.04440", | |
| "title": "Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks" | |
| }, | |
| "1901.08746": { | |
| "arxivId": "1901.08746", | |
| "title": "BioBERT: a pre-trained biomedical language representation model for biomedical text mining" | |
| }, | |
| "1903.10676": { | |
| "arxivId": "1903.10676", | |
| "title": "SciBERT: A Pretrained Language Model for Scientific Text" | |
| }, | |
| "2004.10964": { | |
| "arxivId": "2004.10964", | |
| "title": "Don\u2019t Stop Pretraining: Adapt Language Models to Domains and Tasks" | |
| }, | |
| "2004.10706": { | |
| "arxivId": "2004.10706", | |
| "title": "CORD-19: The Covid-19 Open Research Dataset" | |
| }, | |
| "1804.05685": { | |
| "arxivId": "1804.05685", | |
| "title": "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" | |
| }, | |
| "2210.10341": { | |
| "arxivId": "2210.10341", | |
| "title": "BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining" | |
| }, | |
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| "arxivId": "2009.13081", | |
| "title": "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams" | |
| }, | |
| "2307.06435": { | |
| "arxivId": "2307.06435", | |
| "title": "A Comprehensive Overview of Large Language Models" | |
| }, | |
| "1808.06752": { | |
| "arxivId": "1808.06752", | |
| "title": "Lessons from Natural Language Inference in the Clinical Domain" | |
| }, | |
| "2203.14371": { | |
| "arxivId": "2203.14371", | |
| "title": "MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering" | |
| }, | |
| "1806.04185": { | |
| "arxivId": "1806.04185", | |
| "title": "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature" | |
| }, | |
| "1809.00732": { | |
| "arxivId": "1809.00732", | |
| "title": "emrQA: A Large Corpus for Question Answering on Electronic Medical Records" | |
| }, | |
| "1902.09476": { | |
| "arxivId": "1902.09476", | |
| "title": "MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts" | |
| }, | |
| "2106.03598": { | |
| "arxivId": "2106.03598", | |
| "title": "SciFive: a text-to-text transformer model for biomedical literature" | |
| }, | |
| "2010.06060": { | |
| "arxivId": "2010.06060", | |
| "title": "Bio-Megatron: Larger Biomedical Domain Language Model" | |
| }, | |
| "2304.14454": { | |
| "arxivId": "2304.14454", | |
| "title": "PMC-LLaMA: Further Finetuning LLaMA on Medical Papers" | |
| }, | |
| "1808.09397": { | |
| "arxivId": "1808.09397", | |
| "title": "MedSTS: a resource for clinical semantic textual similarity" | |
| }, | |
| "1904.02181": { | |
| "arxivId": "1904.02181", | |
| "title": "Probing Biomedical Embeddings from Language Models" | |
| }, | |
| "2204.03905": { | |
| "arxivId": "2204.03905", | |
| "title": "BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model" | |
| }, | |
| "2010.03295": { | |
| "arxivId": "2010.03295", | |
| "title": "COMETA: A Corpus for Medical Entity Linking in the Social Media" | |
| }, | |
| "2005.09067": { | |
| "arxivId": "2005.09067", | |
| "title": "Question-driven summarization of answers to consumer health questions" | |
| }, | |
| "2308.09442": { | |
| "arxivId": "2308.09442", | |
| "title": "BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine" | |
| }, | |
| "2305.16326": { | |
| "arxivId": "2305.16326", | |
| "title": "A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations" | |
| }, | |
| "2104.09585": { | |
| "arxivId": "2104.09585", | |
| "title": "ELECTRAMed: a new pre-trained language representation model for biomedical NLP" | |
| }, | |
| "2204.11574": { | |
| "arxivId": "2204.11574", | |
| "title": "A global analysis of metrics used for measuring performance in natural language processing" | |
| }, | |
| "1510.03225": { | |
| "arxivId": "1510.03225", | |
| "title": "Bias-corrected methods for estimating the receiver operating characteristic surface of continuous diagnostic tests" | |
| }, | |
| "1910.13461": { | |
| "arxivId": "1910.13461", | |
| "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" | |
| }, | |
| "1910.10045": { | |
| "arxivId": "1910.10045", | |
| "title": "Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI" | |
| }, | |
| "2203.15556": { | |
| "arxivId": "2203.15556", | |
| "title": "Training Compute-Optimal Large Language Models" | |
| }, | |
| "2212.10403": { | |
| "arxivId": "2212.10403", | |
| "title": "Towards Reasoning in Large Language Models: A Survey" | |
| }, | |
| "2302.00093": { | |
| "arxivId": "2302.00093", | |
| "title": "Large Language Models Can Be Easily Distracted by Irrelevant Context" | |
| }, | |
| "2305.18654": { | |
| "arxivId": "2305.18654", | |
| "title": "Faith and Fate: Limits of Transformers on Compositionality" | |
| }, | |
| "2210.01240": { | |
| "arxivId": "2210.01240", | |
| "title": "Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought" | |
| }, | |
| "2309.12288": { | |
| "arxivId": "2309.12288", | |
| "title": "The Reversal Curse: LLMs trained on \"A is B\" fail to learn \"B is A\"" | |
| }, | |
| "2212.03551": { | |
| "arxivId": "2212.03551", | |
| "title": "Talking about Large Language Models" | |
| }, | |
| "2207.07051": { | |
| "arxivId": "2207.07051", | |
| "title": "Language models show human-like content effects on reasoning" | |
| }, | |
| "2210.13966": { | |
| "arxivId": "2210.13966", | |
| "title": "The debate over understanding in AI\u2019s large language models" | |
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| "2307.02477": { | |
| "arxivId": "2307.02477", | |
| "title": "Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks" | |
| }, | |
| "2309.13638": { | |
| "arxivId": "2309.13638", | |
| "title": "Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve" | |
| }, | |
| "2212.07919": { | |
| "arxivId": "2212.07919", | |
| "title": "ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning" | |
| }, | |
| "2306.09479": { | |
| "arxivId": "2306.09479", | |
| "title": "Inverse Scaling: When Bigger Isn't Better" | |
| }, | |
| "2305.17306": { | |
| "arxivId": "2305.17306", | |
| "title": "Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance" | |
| }, | |
| "2306.07622": { | |
| "arxivId": "2306.07622", | |
| "title": "Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT" | |
| }, | |
| "2309.15402": { | |
| "arxivId": "2309.15402", | |
| "title": "Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future" | |
| }, | |
| "2306.05836": { | |
| "arxivId": "2306.05836", | |
| "title": "Can Large Language Models Infer Causation from Correlation?" | |
| }, | |
| "2205.11502": { | |
| "arxivId": "2205.11502", | |
| "title": "On the Paradox of Learning to Reason from Data" | |
| }, | |
| "2308.13067": { | |
| "arxivId": "2308.13067", | |
| "title": "Causal Parrots: Large Language Models May Talk Causality But Are Not Causal" | |
| }, | |
| "2402.00157": { | |
| "arxivId": "2402.00157", | |
| "title": "Large Language Models for Mathematical Reasoning: Progresses and Challenges" | |
| }, | |
| "2305.15269": { | |
| "arxivId": "2305.15269", | |
| "title": "Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples" | |
| }, | |
| "2310.08559": { | |
| "arxivId": "2310.08559", | |
| "title": "Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement" | |
| }, | |
| "2205.05718": { | |
| "arxivId": "2205.05718", | |
| "title": "Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks" | |
| }, | |
| "2305.13160": { | |
| "arxivId": "2305.13160", | |
| "title": "Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate" | |
| }, | |
| "2306.08189": { | |
| "arxivId": "2306.08189", | |
| "title": "Language models are not naysayers: an analysis of language models on negation benchmarks" | |
| }, | |
| "2404.14082": { | |
| "arxivId": "2404.14082", | |
| "title": "Mechanistic Interpretability for AI Safety - A Review" | |
| }, | |
| "2304.10703": { | |
| "arxivId": "2304.10703", | |
| "title": "ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness" | |
| }, | |
| "2312.04350": { | |
| "arxivId": "2312.04350", | |
| "title": "CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models" | |
| }, | |
| "2405.00208": { | |
| "arxivId": "2405.00208", | |
| "title": "A Primer on the Inner Workings of Transformer-based Language Models" | |
| }, | |
| "2402.19450": { | |
| "arxivId": "2402.19450", | |
| "title": "Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap" | |
| }, | |
| "2404.18824": { | |
| "arxivId": "2404.18824", | |
| "title": "Benchmarking Benchmark Leakage in Large Language Models" | |
| }, | |
| "2306.06548": { | |
| "arxivId": "2306.06548", | |
| "title": "Inductive reasoning in humans and large language models" | |
| }, | |
| "2310.14491": { | |
| "arxivId": "2310.14491", | |
| "title": "Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models" | |
| }, | |
| "2112.11941": { | |
| "arxivId": "2112.11941", | |
| "title": "CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models" | |
| }, | |
| "2303.12023": { | |
| "arxivId": "2303.12023", | |
| "title": "Logical Reasoning over Natural Language as Knowledge Representation: A Survey" | |
| }, | |
| "2206.10591": { | |
| "arxivId": "2206.10591", | |
| "title": "Can Foundation Models Talk Causality?" | |
| }, | |
| "2305.16572": { | |
| "arxivId": "2305.16572", | |
| "title": "Counterfactual reasoning: Testing language models\u2019 understanding of hypothetical scenarios" | |
| }, | |
| "2205.12598": { | |
| "arxivId": "2205.12598", | |
| "title": "RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners" | |
| }, | |
| "2402.08939": { | |
| "arxivId": "2402.08939", | |
| "title": "Premise Order Matters in Reasoning with Large Language Models" | |
| }, | |
| "2305.14010": { | |
| "arxivId": "2305.14010", | |
| "title": "IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions" | |
| }, | |
| "2206.08353": { | |
| "arxivId": "2206.08353", | |
| "title": "Towards Understanding How Machines Can Learn Causal Overhypotheses" | |
| }, | |
| "2402.18312": { | |
| "arxivId": "2402.18312", | |
| "title": "How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning" | |
| }, | |
| "2308.00225": { | |
| "arxivId": "2308.00225", | |
| "title": "Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias" | |
| } | |
| } |