Datasets:

skonan
/

PaperLens-Text

license: other
configs:
  - config_name: arxiv
    data_files:
      - split: papers
        path: arxiv/*.parquet
  - config_name: openreview-iclr
    data_files:
      - split: papers
        path: iclr/*.parquet
tags:
  - academic-paper-review
  - paper-review
  - sharegpt
  - text
language:
  - en
size_categories:
  - 100K<n<1M

PaperLens-Text

Text version of the OpenReview-ICLR and arXiv PaperLens datasets.

Each row is one unique paper. We release all extracted papers — not every paper here is used in our downstream training/eval sets. The papers that are used are denoted by the references field, which lists every internal (release_name, release_split) pair the paper belongs to (a single paper can belong to multiple). reconstruction.py reads this field to materialize the original sharegpt data.json for any of the ~20 publishable text keys.

Configs (subsets)

arxiv — papers from arxiv (per_venue + 21k families + residual + the arxiv side of combined).
openreview-iclr — papers from ICLR via OpenReview (balanced_original + max_rejects + train_50pct/75pct + the iclr side of combined).

from datasets import load_dataset
ds_arxiv = load_dataset("skonan/PaperLens-Text", "arxiv",           split="papers")
ds_iclr  = load_dataset("skonan/PaperLens-Text", "openreview-iclr", split="papers")

Schema

field	type	description
`paper_id`	`string`	arXiv id or OpenReview submission id
`title`	`string`	paper title
`content`	`string`	prompt-stripped body (the full paper body in markdown)
`metadata`	`string`	JSON blob — venue, year, authors, ratings, decision, …
`label`	`string`	`"Accept"` or `"Reject"`
`references`	`list<list<string>>`	each entry is `[release_name, release_split]` — the internal splits this paper belongs to

Reconstructing the sharegpt `data.json` files

reconstruction.py rebuilds any of the publishable internal keys (e.g. arxiv_50_50_21k_text_..._y24up_test) byte-identically from this dataset. Setup + run:

git clone https://github.com/zlab-princeton/PaperLens.git
cd PaperLens/paperlens-training-and-inference
uv sync

# arxiv training set
uv run python scripts/reconstruction.py \
    --hf_text_repo skonan/PaperLens-Text \
    --dataset_keys arxiv_50_50_balanced_per_venue_text_wmetadata_filtered24480_train

# openreview-iclr training set
uv run python scripts/reconstruction.py \
    --hf_text_repo skonan/PaperLens-Text \
    --dataset_keys iclr_2020_2023_2025_2026_85_5_10_balanced_original_text_labelfix_v7_filtered_train

Reconstructed files land in ./data/ by default (override with --data_root <path>): data/<dataset_key>/data.json (sharegpt rows) and data/dataset_info.json (LlamaFactory entry).

The release ships a manifest.json sidecar mapping each internal dataset_info.json key → (release_name, release_split, columns, file_name), so reconstruction reproduces conversations, _metadata, accept_reject_label (where applicable).

License & citation

License: see the PaperLens collection.

@misc{konan2026paperlens,
  title       = {PaperLens: How Predictable Is Paper Acceptance?},
  author      = {Konan, Sachin and Liu, Jonathan and Liu, Zhuang},
  year        = {2026},
  institution = {Princeton University}
}

PaperLens-Text

Configs (subsets)

Schema

Reconstructing the sharegpt data.json files

License & citation

Reconstructing the sharegpt `data.json` files