category: entailment

claim: BERT achieves state-of-the-art results on eleven NLP benchmarks including GLUE and SQuAD 2.0 by fine-tuning a single pre-trained model.

source_text: We introduce a new language representation model called BERT. BERT obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5%, MultiNLI accuracy to 86.7%, SQuAD v1.1 F1 to 93.2, and SQuAD v2.0 F1 to 83.1.

expected_relation: entailment

domain: cs

note: Clear entailment. Claim accurately paraphrases the abstract's core finding. Fine-tuning a single model to achieve SOTA on multiple benchmarks is the BERT paper's central contribution.


claim: Dropout applied during training improves generalization of deep neural networks by preventing co-adaptation of feature detectors.

source_text: We describe a technique called Dropout that addresses the problem of overfitting in neural networks. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different thinned networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights.

note: Entailment. The claim restates the mechanism (preventing co-adaptation) and effect (improved generalization) accurately.


claim: Among patients with resectable non-small-cell lung cancer, adjuvant chemotherapy with cisplatin-based regimens significantly improves five-year overall survival compared to surgery alone.

source_text: We performed a meta-analysis of randomized trials comparing cisplatin-based adjuvant chemotherapy to observation after complete surgical resection in patients with non-small-cell lung cancer. Adjuvant chemotherapy was associated with a significant improvement in overall survival (HR 0.89; 95% CI 0.82–0.96; p=0.005), corresponding to an absolute benefit of 5.4% at 5 years (from 60.4% to 65.8%).

domain: biomedical

note: Entailment. Claim correctly summarizes the meta-analytic finding of significant OS benefit. The 5-year frame and 'surgery alone' comparator are accurate.


claim: Stereotype threat impairs the performance of negatively stereotyped groups on standardized tests.

source_text: When a negative intellectual stereotype about one's group could be applied to oneself in a testing situation, we hypothesized that this would interfere with intellectual functioning. In three experiments, Black students who were told that a test was diagnostic of intellectual ability performed significantly worse than Black students who were told the test was not diagnostic. White students showed no such pattern. These results suggest that stereotype threat — the risk of confirming a negative stereotype about one's group — can depress the intellectual performance of group members.

domain: psychology

note: Entailment. The claim accurately captures Steele & Aronson's core finding. 'Negatively stereotyped groups' and 'standardized tests' match the experimental setup.


claim: Socioeconomic status in childhood is a significant predictor of adult health outcomes, independent of adult socioeconomic position.

source_text: This study examined the relationship between childhood socioeconomic circumstances and adult health using data from a prospective cohort (n=11,441). After adjusting for adult socioeconomic position, education, and health behaviors, childhood poverty was independently associated with higher rates of cardiovascular disease (OR 1.38, 95% CI 1.19–1.60), diabetes (OR 1.24, 95% CI 1.08–1.43), and all-cause mortality (HR 1.31, 95% CI 1.15–1.49) in middle adulthood.

note: Entailment. Claim correctly states independent effect of childhood SES. Source explicitly adjusts for adult SES, confirming the independent pathway.


claim: Transformer models pre-trained on large corpora can be fine-tuned with very few labeled examples to achieve competitive performance on downstream NLP tasks.

source_text: We demonstrate that pre-trained language model representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. In low-resource settings with as few as 100 labeled examples, fine-tuned BERT models outperform task-specific models trained on the full dataset.

note: Entailment. 'Very few labeled examples' aligns with the source's 'as few as 100 labeled examples' and 'low-resource settings'. The performance advantage is confirmed.


category: contradiction

claim: Adding aspirin to standard anticoagulation therapy significantly reduces the risk of recurrent venous thromboembolism by approximately 30% in patients who have completed initial treatment.

source_text: In a randomized, double-blind trial of 822 patients with unprovoked venous thromboembolism who had completed initial anticoagulation, we compared aspirin 100 mg daily with placebo. The rate of recurrent VTE was 6.6% per year in the aspirin group and 11.2% per year in the placebo group (HR 0.58, 95% CI 0.36–0.93). However, when we adjusted for aspirin's antiplatelet effect on arterial events, the absolute benefit on VTE recurrence alone was 1.3% per year and did not reach statistical significance (p=0.18) after sensitivity analysis excluding arterial events.

expected_relation: contradiction

note: CONTRADICTION. The claim states a '30% reduction' that is 'significant'. The source shows 42% relative reduction in crude rate (HR 0.58) but the VTE-specific effect did not reach significance after sensitivity analysis. The claim overstates the magnitude and confidence. Subtly plausible because the crude HR is indeed ~0.58.


claim: Multitasking improves productivity by allowing workers to complete multiple tasks in parallel, as workers who multitask report higher job satisfaction and output.

source_text: We conducted a controlled experiment in which 128 participants completed cognitive tasks under single-task and multitask conditions. Multitasking increased the time required to complete both tasks by an average of 40% compared to sequential processing. Self-reported satisfaction was higher in the multitasking condition despite objectively lower performance. Workers who believed they were multitasking effectively showed systematic overconfidence in their output quality.

note: CONTRADICTION. The claim asserts multitasking improves productivity. The source finds 40% longer completion times (productivity decrease). The source does note higher self-reported satisfaction, which a selective reader might use to construct the misleading claim — a cherry-pick combined with inversion.


claim: Social media use in adolescents is causally linked to depression, with each additional hour of use per day increasing depressive symptom scores by a clinically meaningful margin.

source_text: Using data from the UK Millennium Cohort Study (n=10,894 adolescents aged 14), we examined the longitudinal relationship between social media use and depressive symptoms. After controlling for baseline depression, family SES, and peer relationships, social media use at age 14 was associated with depressive symptoms at age 16 (β=0.06, 95% CI 0.02–0.10). Effect sizes were small and did not meet conventional thresholds for clinical significance (d<0.1). No dose-response relationship was observed for boys. We conclude that social media's role in adolescent depression is modest and likely not causal.

note: CONTRADICTION. The claim asserts a causal link and 'clinically meaningful' increase per hour. The source explicitly states effect sizes are below clinical significance thresholds, no dose-response for boys, and 'likely not causal'. A common misrepresentation in media coverage of correlational cohort studies.


claim: Retrieval-augmented generation eliminates hallucination in large language models by grounding responses in retrieved document passages.

source_text: We introduce Retrieval-Augmented Generation (RAG), which combines pre-trained parametric and non-parametric memory for language generation. RAG reduces hallucination compared to parametric-only models: on TriviaQA, RAG achieves 68.0% EM vs 52.1% for GPT-2. However, RAG does not eliminate hallucination entirely. When retrieved documents are irrelevant or out-of-date, the model can still generate factually incorrect statements. Hallucination rates remain non-trivial even with retrieval augmentation.

note: CONTRADICTION. The claim says RAG 'eliminates' hallucination. Source explicitly states it does not eliminate it and that non-trivial hallucination remains. The word 'eliminates' vs 'reduces' is the crux — a subtle but consequential distortion common in deployment-focused papers citing RAG.


claim: The minimum wage increases studied by Dube et al. reduced employment in the restaurant sector by approximately 2–3% per 10% wage increase.

source_text: We use county-pair differences to control for regional heterogeneity in examining the effects of minimum wage increases on restaurant employment. Comparing counties on either side of state borders, we find that minimum wage increases have no discernible employment effect in the restaurant sector. The estimated employment elasticity is -0.01 (SE 0.07), not statistically distinguishable from zero. Our results contradict the consensus view of approximately 1–2% employment loss per 10% wage increase.

note: CONTRADICTION. The claim attributes a '2–3% loss' figure to Dube et al. The actual Dube, Lester & Reich (2010) paper finds near-zero employment effects (elasticity ≈ -0.01) and explicitly contradicts the elasticity the claim attributes to them.


category: neutral

claim: The Adam optimizer outperforms SGD with momentum on all image classification tasks when training from scratch on ImageNet.

source_text: We propose Adam, a stochastic optimization algorithm that computes adaptive learning rates for each parameter using estimates of first and second moments of the gradients. Empirically, we demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods on logistic regression, multilayer fully connected neural networks, and recurrent neural networks, including language modeling and speech recognition.

expected_relation: neutral

note: NEUTRAL/UNSUPPORTED. The claim makes a universal assertion about image classification and ImageNet specifically. The Adam paper's benchmarks cover logistic regression, MLPs, and RNNs but do NOT include ImageNet-scale CNN training — the domain where SGD often outperforms Adam in practice.


claim: Meditation interventions reduce cortisol levels in healthy adults by at least 20% after eight weeks of practice.

source_text: We conducted a randomized controlled trial of mindfulness-based stress reduction (MBSR) in 89 healthy adults. After 8 weeks, participants in the MBSR group showed significant reductions in self-reported stress and anxiety (Cohen's d=0.58) compared to waitlist controls. No blood samples were collected; cortisol was not measured in this study.

note: NEUTRAL/UNSUPPORTED. The claim specifically concerns cortisol levels, a biomarker not measured in the source study. The source measures subjective stress and anxiety only. The claim is neither supported nor contradicted — it addresses a variable the study did not examine.


claim: Large language models exhibit in-context learning primarily because they perform implicit Bayesian inference over possible programs.

source_text: We study in-context learning (ICL) in large language models, where a model performs a task given a few demonstrations without parameter updates. We show that ICL performance scales predictably with model size and number of demonstrations. Models with more than 100B parameters show emergent ICL capabilities on tasks where smaller models fail entirely. The mechanism underlying ICL remains an open question.

note: NEUTRAL/UNSUPPORTED. The claim asserts a specific mechanistic explanation (Bayesian inference over programs). The source describes the phenomenon and its scaling properties but explicitly states the mechanism is an open question. The causal claim goes beyond what the source establishes.


claim: Exposure to green spaces in urban environments reduces violent crime rates by displacing potential offenders to public parks.

source_text: A longitudinal study of 21 U.S. cities found that increases in urban green space were associated with reductions in violent crime (β=-0.14, p<0.05) over a 10-year period. The association persisted after controlling for population density, income, and police presence. The authors note that the causal mechanism is unknown, and multiple pathways including social cohesion, temperature reduction, and territorial reclamation are plausible.

note: NEUTRAL/UNSUPPORTED. The claim attributes a specific mechanism ('displacing potential offenders to parks') that the source paper does not identify. The source reports the association and explicitly states the mechanism is unknown. Mechanism imputation is a common form of claim inflation.


claim: The gut microbiome composition in patients with major depressive disorder is indistinguishable from that of healthy controls when matched for diet and BMI.

source_text: We performed 16S rRNA sequencing on stool samples from 127 patients with MDD and 123 healthy controls. Patients with MDD showed significant reductions in Faecalibacterium prausnitzii (p=0.002) and Bifidobacterium longum (p=0.004). Importantly, our cohort was not matched for dietary pattern or BMI, which are potential confounders. Whether the observed differences would persist after such matching remains to be determined.

note: NEUTRAL/UNSUPPORTED. The claim asserts equivalence when matched for diet and BMI. The source study explicitly did NOT match for these confounders and flags this as a limitation. The source actually finds differences, but whether those differences survive matching is unresolved.


claim: Transfer learning from ImageNet-pretrained models consistently improves performance across all computer vision tasks, including medical image analysis.

source_text: We show that features learned from ImageNet transfer effectively to natural image tasks such as object detection and scene recognition, reducing the need for large task-specific datasets. Transfer learning improved performance by 4–8% on Pascal VOC and COCO benchmarks. Note that these results apply to natural images; transfer to specialized domains such as histopathology or radiology may require domain adaptation due to the substantial visual distribution shift.

note: EDGE CASE — overgeneralization. The source shows strong transfer benefits for natural image tasks but explicitly caveats that specialized medical domains (histopathology, radiology) require domain adaptation. The claim removes this scope condition and generalizes to 'all computer vision tasks including medical'.


claim: The drug canagliflozin significantly reduces the risk of major adverse cardiovascular events in patients with type 2 diabetes.

source_text: The CANVAS trial randomized 10,142 patients with type 2 diabetes and high cardiovascular risk to canagliflozin or placebo. Canagliflozin reduced the composite of major adverse cardiovascular events (MACE: nonfatal MI, nonfatal stroke, or CV death) by 14% (HR 0.86, 95% CI 0.75–0.97; p=0.02). However, canagliflozin was associated with a nearly doubled risk of lower-limb amputation (HR 1.97, 95% CI 1.41–2.75; p<0.001). The benefit-risk profile requires careful consideration in patients at high amputation risk.

note: EDGE CASE — cherry-picking. The claim is technically supported for MACE (HR 0.86, p=0.02). However, by omitting the doubled amputation risk, the claim presents a selective and misleading picture of the drug's profile. This tests whether the signal fires for incomplete, positively-biased representation of mixed results.


claim: The best-performing model on the SuperGLUE benchmark is T5-11B, which achieved a score of 89.3, surpassing the human baseline.

source_text: We introduce the T5 (Text-to-Text Transfer Transformer) framework, which unifies NLP tasks under a text-to-text format. Our largest model, T5-11B, achieves 89.3 on SuperGLUE, surpassing the human baseline of 89.8 on several subtasks while matching it overall. At the time of submission (October 2019), T5-11B holds the top position on the SuperGLUE leaderboard.

note: EDGE CASE — outdated claim. At the time of the T5 paper (2019), the claim was accurate. By 2021+ many models surpassed T5-11B on SuperGLUE. A paper citing this in 2024 as the 'best-performing model' would be using an outdated claim. The source itself qualifies: 'at the time of submission'. Tests whether temporal context flags the currency problem.


claim: The Transformer architecture, which relies exclusively on self-attention and dispensing with recurrence, achieves superior results on machine translation compared to recurrent sequence models.

source_text: We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show the Transformer generalizes well to other tasks by applying it successfully, achieving 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU.

note: EDGE CASE — paraphrase entailment. The claim rephrases the abstract in different words: 'relies exclusively on self-attention' paraphrases 'based solely on attention mechanisms', 'dispensing with recurrence' is near-verbatim, and 'superior results on machine translation' paraphrases the BLEU improvement. Tests that the NLI model handles paraphrase as entailment rather than flagging it as suspicious.