README.md

metadata

license: cc-by-4.0
task_categories:
  - text-generation
  - text-classification
  - summarization
  - question-answering
language:
  - en
tags:
  - peer-review
  - openreview
  - scientific-papers
  - nlp
  - benchmarking
  - meta-science
pretty_name: >-
  ReviewArena: A Large-Scale Cross-Conference Dataset and Benchmark for LLM Peer
  Review
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: neurips
        path: data/neurips-*.parquet
      - split: iclr
        path: data/iclr-*.parquet
      - split: icml
        path: data/icml-*.parquet
      - split: tmlr
        path: data/tmlr-*.parquet
      - split: emnlp
        path: data/emnlp-*.parquet
      - split: corl
        path: data/corl-*.parquet
      - split: colm
        path: data/colm-*.parquet

ReviewArena

ReviewArena accompanies the NeurIPS Evaluations & Datasets submission ReviewArena: A Large-Scale Cross-Conference Dataset and Benchmark for LLM Peer Review.

This release is a large, multi-conference corpus of peer-reviewed papers + their reviews + author rebuttals + acceptance decisions, harvested from OpenReview and aligned with OCR'd full-text markdown of each paper PDF where available.

• 51,529 papers
• 196,099 reviews
• 558,785 OCR'd PDF pages (markdown inlined per row)
• 7 conferences, 22 venue/year combinations, 2020 – 2026

from datasets import load_dataset

# Use the Hugging Face dataset ID supplied on OpenReview for this submission.
ds = load_dataset("<anonymous-hub-id>/reviewarena")
print(ds)
# DatasetDict({
#   neurips: Dataset(num_rows=...)
#   iclr:    Dataset(num_rows=...)
#   icml:    Dataset(num_rows=...)
#   tmlr:    Dataset(num_rows=...)
#   emnlp:   Dataset(num_rows=...)
#   corl:    Dataset(num_rows=...)
#   colm:    Dataset(num_rows=...)
# })

Coverage

One split per conference family. Within a split, filter by year, venue_id, or track for slicing.

Split	Venues / years	Tracks	≈ Papers
neurips	2021, 2022, 2023, 2023 D&B, 2024, 2025	main + Datasets & Benchmarks (2023)	~18,400
iclr	2020, 2021, 2022, 2023, 2024, 2025, 2026	main	~22,500
icml	2025	main	~3,300
tmlr	rolling (all accepted papers as of April 2026)	main	~3,750
emnlp	2023	main + Findings	~2,000
corl	2021, 2022, 2023, 2024	main	~820
colm	2024, 2025	main	~720

NeurIPS 2020 and earlier used CMT and have no public OpenReview reviews.

Schema

Each row is one paper.

Column	Type	Notes
forum_id	string	OpenReview forum ID — primary key
conference	string	neurips / iclr / icml / tmlr / emnlp / corl / colm
year	int32	Conference year
track	string	main / datasets_and_benchmarks / findings / etc.
venue_id	string	OpenReview venue ID, e.g. NeurIPS.cc/2024/Conference
paper_number	int32	Submission number (nullable)
title	string
abstract	string
authors	list<string>
keywords	list<string>
tldr	string
primary_area	string
venue	string	Final venue string, e.g. "NeurIPS 2024 poster"
decision	string	Accept (poster), Accept (oral), Reject, etc.
decision_comment	string	Area-chair meta-review
author_rebuttal	string	General rebuttal (≤2024); empty when per-reviewer rebuttals are used
num_reviews	int32	Convenience count
reviews_json	string	All reviews as a JSON string — see schema below
markdown	string	OCR'd full text of the PDF (see OCR below); "" if PDF unavailable
markdown_chars	int64	len(markdown) for fast filtering

Decoding reviews_json

reviews_json is json.dumps(list[dict]). Decode with:

import json
df = ds["neurips"].to_pandas()
df["reviews"] = df["reviews_json"].map(json.loads)
print(df.iloc[0]["reviews"][0].keys())

Each review dict is a union over all forms used by all venues across all years, with absent fields as empty strings / None. The most reliably populated fields are:

Field	Where populated
review_id, reviewer, rating, confidence, rebuttal	All venues
summary, questions, limitations, strengths, weaknesses	NeurIPS 2022–24, ICLR, ICML, CoRL, COLM
soundness, presentation, contribution	NeurIPS 2022–24, ICLR, ICML
quality, clarity, significance, originality	NeurIPS 2025, TMLR
strengths_and_weaknesses	NeurIPS 2025 (merged form)
was_revised, final_justification	NeurIPS 2025 (in-place review revisions)
claims_and_evidence, theoretical_claims, experimental_designs_or_analyses, relation_to_broader_scientific_literature, essential_references_not_discussed	TMLR
paper_topic_and_main_contributions, reasons_to_accept, reasons_to_reject, excitement, reproducibility, ethical_concerns	EMNLP 2023
summary_of_paper, summary_of_recommendation, technical_quality, clarity_of_presentation, potential_impact, robotics_focus	CoRL
extra_scores, extra_text	dicts capturing any venue-specific fields not in the union

Schema differences in detail:

• NeurIPS 2021 D&B (track): older form, most numeric sub-scores absent; lives in the neurips split.
• NeurIPS 2022–2024: soundness/presentation/contribution; separate strengths/weaknesses; single general author_rebuttal.
• NeurIPS 2025: quality/clarity/significance/originality; merged strengths_and_weaknesses; per-reviewer rebuttals; in-place review revisions tracked via was_revised + final_justification.
• ICLR 2020–2026: NeurIPS-style soundness/presentation/contribution; per-reviewer rebuttals.
• ICML 2025: similar to NeurIPS-style; some venue-specific fields (e.g. technical_quality, novelty) live in extra_scores.
• TMLR: claim-evidence-style structured review; rolling acceptance.
• EMNLP 2023: ARR-style review form with reasons_to_accept/reasons_to_reject/excitement/reproducibility.
• CoRL: robotics-focused review form.
• COLM: language-modeling-focused review form.

Source and collection

• Source: OpenReview — main and track-level conferences (including NeurIPS Datasets & Benchmarks where applicable).
• Collected: April 2026 via the OpenReview Python API (a mix of openreview.api v2 and legacy v1 for older NeurIPS/ICLR years).
• Pipeline: parallel Modal workers with rate-limit-aware scheduling; each forum includes official reviews, official comments, decisions, and author rebuttals where exposed by OpenReview; PDFs ingested for OCR.

Markdown / OCR

• Engine: nvidia/nemotron-ocr-v2
• Compute: multi-GPU Modal deployment (~6 h wall-clock end-to-end for the full OCR pass referenced in the paper)
• Throughput: ~7,200 PDFs/hour aggregate; mean 7.3 s/PDF per worker; 558,785 pages processed
• Failures: 1 PDF errored during OCR; ~76 PDFs were unavailable from OpenReview at scrape time and have markdown == "". Use markdown_chars > 0 to filter.

OCR quality (spot-checked across NeurIPS, ICLR, ICML, TMLR, CoRL, COLM)

What works well:

• Body prose, abstracts, section headers, paragraph structure
• In-line citations like (Author et al., YEAR) mostly preserved
• Equations rendered linearly (variable names + structure visible)
• Page boundaries marked with \n\n---\n\n separator

Recurring artifacts to be aware of (consistent across venues, low impact for most NLP tasks):

• Email addresses and URLs containing repeated chars (e.g. name@@@cmu.eed, https:////aaaaaaa)
• Occasional word-doubling at line breaks (decision-decision-making, Complex-Valuee Valued)
• Citation lists missing semicolons ((Singer 2007 Uhlhaas et al. 2009))
• Greek letters and super/sub-scripts often dropped or flattened
• First-page logo/header text occasionally bleeds into title (git Cooperative …)
• Figure caption tokens interleave with body text on figure-heavy pages

Practical impact: fine for dense retrieval, language modeling, review-grounding, summarization. Not suitable for tasks requiring exact equations or canonical citation strings.

Suggested uses

• Train review-quality classifiers / score predictors
• Study reviewer agreement, rebuttal effectiveness, decision dynamics
• Build retrieval-augmented or grounded scientific-paper assistants
• Meta-research on the peer-review process across venues and years
• Few-shot / RAG benchmarks that require the paper full text + the reviews

Benchmark

Benchmark

ReviewArena-Eval is the companion benchmark slice: a fixed 1,002-paper subset (167 papers per numerically rated venue — NeurIPS, ICLR, ICML, CoRL, COLM, EMNLP) with venue-year-aware prompts that respect each venue's native rating scale and sub-score axes. See anonymousNeurIPS2026submission4281/reviewarena-eval for the dataset card, evaluation protocol, and baseline results across six open-weight LLMs.

from datasets import load_dataset
bench = load_dataset("anonymousNeurIPS2026submission4281/reviewarena-eval", split="train")

Citation

Under double-blind review. Please cite the camera-ready NeurIPS proceedings entry once public; until then use an anonymous placeholder consistent with your venue’s reviewer guidelines.

Terms

Use is subject to the redistribution conditions stated on the reviewer-visible Hugging Face dataset card for this submission (including applicable dataset licenses and attribution expectations after deanonymization).

Repository layout (development snapshot)

Anonymized code intended for the OpenReview code URL is maintained under review-arena/. LaTeX sources and submission packaging for the NeurIPS track may appear elsewhere in this repository tree during preparation.

Acknowledgements

• The OpenReview team for maintaining open peer-review archives.
• NVIDIA for nemotron-ocr-v2.
• Modal for elastic GPU and storage capacity used in corpus construction.