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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`](https://github.com/zlab-princeton/PaperLens/blob/main/paperlens-training-and-inference/scripts/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).
```python
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`](https://github.com/zlab-princeton/PaperLens/blob/main/paperlens-training-and-inference/scripts/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:
```bash
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](https://huggingface.co/collections/skonan/paperlens-6a0c79da423c3a436b7f6b1a).
```bibtex
@misc{konan2026paperlens,
title = {PaperLens: How Predictable Is Paper Acceptance?},
author = {Konan, Sachin and Liu, Jonathan and Liu, Zhuang},
year = {2026},
institution = {Princeton University}
}
```
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