metadata
license: cc-by-nc-4.0
pretty_name: PaperGuard
task_categories:
- image-text-to-text
- text-generation
language:
- en
size_categories:
- 1K<n<10K
tags:
- peer-review
- adversarial-robustness
- prompt-injection
- multimodal
- benchmark
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
PaperGuard
A benchmark of academic papers (text + key figures) for evaluating the robustness of multimodal AI peer-review systems.
Paper: arXiv:2606.12716 — Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review · Project Pages: paper-guard.github.io
Schema (single test split)
| column | type | description |
|---|---|---|
paper_id |
string | paper identifier (e.g. 502, ICLR2020_10, 1-26) |
source |
string | one of iclr_2017, AgentReview, F1000 |
title |
string | paper title (may be null) |
abstract |
string | abstract text |
num_figures |
int | number of figures present (0–2) |
paper_json |
string | the full original parsed-paper JSON ({"name", "metadata"}), verbatim |
method_figure |
image | the method figure (<id>-1.png), or null |
result_figure |
image | the result figure (<id>-2.png), or null |
Usage
from datasets import load_dataset
ds = load_dataset("rellabear/PaperGuard", split="test")
row = ds[0]
fig = row["method_figure"] # PIL.Image or None
License & Attribution
Released for non-commercial research under CC-BY-NC-4.0. Please cite the original sources:
- F1000 — via NLPeer (Dycke et al., ACL 2023); F1000Research content is CC-BY.
- iclr_2017 — via PeerRead (Kang et al., NAACL 2018).
- AgentReview — via AgentReview (Jin et al., EMNLP 2024).
ICLR/OpenReview-derived content remains subject to OpenReview's terms.