prompt stringlengths 4.79k 69.2k | category stringclasses 1
value | extra_info dict |
|---|---|---|
<|im_start|>system
You are a research assistant that answers questions about scientific papers. Read the paper carefully and provide accurate, concise answers based on the content.<|im_end|>
<|im_start|>user
Below is a scientific paper. Read it carefully and answer the question.
<|memory_start|>
Title: End-to-End Trai... | memwrap/qasper | {
"ground_truth": {
"answer": "3,044 sentences in 100 dialogs",
"answer_type": "extractive"
},
"scoring_function": "qasper_log_perplexity",
"paper_id": "1911.10742",
"question_id": "397a1e851aab41c455c2b284f5e4947500d797f0",
"variant": "memwrap"
} |
<|im_start|>system
You are a research assistant that answers questions about scientific papers. Read the paper carefully and provide accurate, concise answers based on the content.<|im_end|>
<|im_start|>user
Below is a scientific paper. Read it carefully and answer the question.
<|memory_start|>
Title: End-to-End Trai... | memwrap/qasper | {
"ground_truth": {
"answer": "using a role-playing task on the Amazon Mechanical Turk platform and collecting typed conversations",
"answer_type": "free_form"
},
"scoring_function": "qasper_log_perplexity",
"paper_id": "1911.10742",
"question_id": "cc8b4ed3985f9bfbe1b5d7761b31d9bd6a965444",
"varian... |
<|im_start|>system
You are a research assistant that answers questions about scientific papers. Read the paper carefully and provide accurate, concise answers based on the content.<|im_end|>
<|im_start|>user
Below is a scientific paper. Read it carefully and answer the question.
<|memory_start|>
Title: End-to-End Trai... | memwrap/qasper | {
"ground_truth": {
"answer": "TransferTransfo and Hybrid",
"answer_type": "free_form"
},
"scoring_function": "qasper_log_perplexity",
"paper_id": "1911.10742",
"question_id": "f7662b11e87c1e051e13799413f3db459ac3e19c",
"variant": "memwrap"
} |
<|im_start|>system
You are a research assistant that answers questions about scientific papers. Read the paper carefully and provide accurate, concise answers based on the content.<|im_end|>
<|im_start|>user
Below is a scientific paper. Read it carefully and answer the question.
<|memory_start|>
Title: End-to-End Trai... | memwrap/qasper | {
"ground_truth": {
"answer": "Perplexity Response-Intent Prediction (RIP) Response-Slot Prediction (RSP) Extended Response-Intent Prediction (ERIP) Extended Response-Slot Prediction (ERSP) Fluency Coherence Engagement Dialog length Task Success Score (TaskSuc)",
"answer_type": "extractive"
},
"scoring_fu... |
<|im_start|>system
You are a research assistant that answers questions about scientific papers. Read the paper carefully and provide accurate, concise answers based on the content.<|im_end|>
<|im_start|>user
Below is a scientific paper. Read it carefully and answer the question.
<|memory_start|>
Title: OpenTapioca: Li... | memwrap/qasper | {
"ground_truth": {
"answer": "The model improves the state of the art performance for the ISTEX dataset (F1 micro: 0.870, F1 macro: 0.858) and for the Microposts 2016 dataset (F1 micro: 0.087).",
"answer_type": "free_form"
},
"scoring_function": "qasper_log_perplexity",
"paper_id": "1904.09131",
"qu... |
"<|im_start|>system\nYou are a research assistant that answers questions about scientific papers. Re(...TRUNCATED) | memwrap/qasper | {"ground_truth":{"answer":"two state-of-the-art early rumour detection baselines Liu et. al (2015) a(...TRUNCATED) |
"<|im_start|>system\nYou are a research assistant that answers questions about scientific papers. Re(...TRUNCATED) | memwrap/qasper | {"ground_truth":{"answer":"accuracy to evaluate effectiveness Detection Error Trade-off (DET) curves(...TRUNCATED) |
"<|im_start|>system\nYou are a research assistant that answers questions about scientific papers. Re(...TRUNCATED) | memwrap/qasper | {"ground_truth":{"answer":"No. They additionally use similarity to previously detected rumors to mak(...TRUNCATED) |
"<|im_start|>system\nYou are a research assistant that answers questions about scientific papers. Re(...TRUNCATED) | memwrap/qasper | {"ground_truth":{"answer":"Yes","answer_type":"yes_no"},"scoring_function":"qasper_log_perplexity","(...TRUNCATED) |
"<|im_start|>system\nYou are a research assistant that answers questions about scientific papers. Re(...TRUNCATED) | memwrap/qasper | {"ground_truth":{"answer":"Chinese","answer_type":"free_form"},"scoring_function":"qasper_log_perple(...TRUNCATED) |
End of preview. Expand in Data Studio
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}
}
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