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configs:
- config_name: memwrap
data_files:
- split: test
path: memwrap/qasper.jsonl
- config_name: plain
data_files:
- split: test
path: plain/qasper.jsonl
---
# QASPER Benchmark
Question Answering on Scientific Papers - NLP research paper comprehension benchmark.
## Overview
| Metric | Value |
|--------|-------|
| Papers | 416 (test set) |
| Questions | 1,370 (answerable) |
| Answer Types | Free-form, extractive, yes/no |
| Context | Full paper (title, abstract, sections) |
## Source
Based on [QASPER](https://allenai.org/data/qasper) dataset by AllenAI.
Paper: [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://aclanthology.org/2021.naacl-main.365/)
## Variants
- **memwrap**: Paper content wrapped with `<|memory_start|>` / `<|memory_end|>` tags
- **plain**: Raw paper content without memory tags
## Usage
```python
from datasets import load_dataset
# Load memwrap variant
ds = load_dataset("tonychenxyz/qasper", "memwrap", split="test")
# Load plain variant
ds = load_dataset("tonychenxyz/qasper", "plain", split="test")
```
## Scoring
Uses `qasper_log_perplexity` scoring function:
- Evaluates model performance using log perplexity of generated answer tokens
- Lower log perplexity indicates better performance
- Matches the perplexity-based evaluation used in cardridge baselines
Target answers are stored in `extra_info.ground_truth.answer`.
## Citation
```bibtex
@inproceedings{dasigi2021qasper,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Dasigi, Pradeep and Lo, Kyle and Beltagy, Iz and Cohan, Arman and Smith, Noah A and Gardner, Matt},
booktitle={NAACL},
year={2021}
}
```
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