| --- |
| 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](https://openreview.net) 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** |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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](https://openreview.net) — main and any track-level conferences (e.g. NeurIPS Datasets & Benchmarks). |
| - **Collected**: April 2026 via the [OpenReview Python API](https://github.com/openreview/openreview-py) (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`](https://huggingface.co/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](https://creativecommons.org/licenses/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](https://openreview.net) team for keeping the peer-review record open. |
| - NVIDIA for releasing [nemotron-ocr-v2](https://huggingface.co/nvidia/nemotron-ocr-v2). |
| - [Modal](https://modal.com) for the GPU and storage infrastructure used to assemble this corpus. |
|
|
