metadata
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 dataset by AllenAI.
Paper: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Variants
- memwrap: Paper content wrapped with
<|memory_start|>/<|memory_end|>tags - plain: Raw paper content without memory tags
Usage
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
@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}
}