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Datasets:
Samarth0710
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reviewbench

Tasks:
Text Generation
Text Classification
Summarization
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
peer-review
openreview
scientific-papers
nlp
benchmarking
meta-science
Libraries:
Datasets
Dask
Polars
License:
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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: ReviewBench — A Multi-Conference Peer-Review Corpus
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: neurips
        path: data/neurips-*
      - split: iclr
        path: data/iclr-*
      - split: icml
        path: data/icml-*
      - split: tmlr
        path: data/tmlr-*
      - split: emnlp
        path: data/emnlp-*
      - split: corl
        path: data/corl-*
      - split: colm
        path: data/colm-*
dataset_info:
  features:
    - name: forum_id
      dtype: string
    - name: conference
      dtype: string
    - name: year
      dtype: int32
    - name: track
      dtype: string
    - name: venue_id
      dtype: string
    - name: paper_number
      dtype: int32
    - name: title
      dtype: string
    - name: abstract
      dtype: string
    - name: authors
      list: string
    - name: keywords
      list: string
    - name: tldr
      dtype: string
    - name: primary_area
      dtype: string
    - name: venue
      dtype: string
    - name: decision
      dtype: string
    - name: decision_comment
      dtype: string
    - name: author_rebuttal
      dtype: string
    - name: num_reviews
      dtype: int32
    - name: reviews_json
      dtype: string
    - name: markdown
      dtype: string
    - name: markdown_chars
      dtype: int64
  splits:
    - name: colm
      num_bytes: 69559593
      num_examples: 717
    - name: corl
      num_bytes: 55415874
      num_examples: 813
    - name: emnlp
      num_bytes: 175864122
      num_examples: 2009
    - name: iclr
      num_bytes: 2043218314
      num_examples: 22532
    - name: icml
      num_bytes: 387957515
      num_examples: 3257
    - name: neurips
      num_bytes: 2023212137
      num_examples: 18453
    - name: tmlr
      num_bytes: 400634995
      num_examples: 3748
  download_size: 2728439622
  dataset_size: 5155862550

ReviewBench

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 every paper.

  • 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
ds = load_dataset("/reviewbench")
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 any track-level conferences (e.g. NeurIPS Datasets & Benchmarks).
  • Collected: April 2026 via the OpenReview Python API (a mix of openreview.api v2 and legacy v1 for older NeurIPS/ICLR years).
  • Scraping pipeline: parallel Modal workers (100 containers, single-token reuse to bypass the 3-req/min rate limit). Each forum was fetched with all official reviews, official comments, decision, and author rebuttals; PDFs were downloaded to a Modal volume.

Markdown / OCR

  • Engine: nvidia/nemotron-ocr-v2
  • Compute: 10× NVIDIA L40S GPUs in parallel on Modal (~6 h wall-clock end-to-end)
  • Throughput: ~7,200 PDFs/hour aggregate; mean 7.3 s/PDF per worker; 558,785 pages processed
  • Failures: 1 PDF errored out 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

Related work

This dataset was assembled in support of an ICML 2026 Datasets and Benchmarks-track submission introducing ReviewBench. Citation will be updated upon publication.

License

Released under CC BY 4.0. Paper full text and review text remain the intellectual property of their respective authors; this dataset redistributes them for non-commercial research consistent with OpenReview's public-access policy. If you are an author and would like content removed, please open an issue on the dataset repository.

Acknowledgements

  • The OpenReview team for keeping the peer-review record open.
  • NVIDIA for releasing nemotron-ocr-v2.
  • Modal for the GPU and storage infrastructure used to assemble this corpus.