SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation

• URL: http://arxiv.org/abs/2405.09939v2
• Date: Wed, 10 Jul 2024 01:25:50 GMT
• Title: SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
• Authors: Yuwei Wan, Yixuan Liu, Aswathy Ajith, Clara Grazian, Bram Hoex, Wenjie Zhang, Chunyu Kit, Tong Xie, Ian Foster,
• Abstract summary: SciQAG is a framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs) We construct a large-scale, high-quality, open-ended science QA dataset containing 188,042 QA pairs extracted from 22,743 scientific papers across 24 scientific domains. We also introduce SciQAG-24D, a new benchmark task designed to evaluate the science question-answering ability of LLMs.
• Score: 11.129800893611646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers. Utilizing this framework, we construct a large-scale, high-quality, open-ended science QA dataset containing 188,042 QA pairs extracted from 22,743 scientific papers across 24 scientific domains. We also introduce SciQAG-24D, a new benchmark task designed to evaluate the science question-answering ability of LLMs. Extensive experiments demonstrate that fine-tuning LLMs on the SciQAG dataset significantly improves their performance on both open-ended question answering and scientific tasks. To foster research and collaboration, we make the datasets, models, and evaluation codes publicly available, contributing to the advancement of science question answering and developing more interpretable and reasoning-capable AI systems.

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