Summary of Sciqag: a Framework For Auto-generated Science Question Answering Dataset with Fine-grained Evaluation, by Yuwei Wan et al.
SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
by Yuwei Wan, Yixuan Liu, Aswathy Ajith, Clara Grazian, Bram Hoex, Wenjie Zhang, Chunyu Kit, Tong Xie, Ian Foster
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The SciQAG framework uses large language models (LLMs) to generate high-quality science question-answer pairs from a vast corpus of scientific literature. It consists of two components: a QA generator and a QA evaluator, which work together to extract diverse research-level questions and answers from papers across 24 domains. The framework creates a massive dataset containing 188,042 QA pairs extracted from 22,743 scientific papers. Additionally, the SciQAG-24D benchmark task is designed to evaluate LLMs’ science question-answering abilities. Experimental results show that fine-tuning LLMs on the SciQAG dataset significantly improves their performance on open-ended question answering and scientific tasks. The datasets, models, and evaluation codes are made publicly available to foster research and collaboration, ultimately advancing science question answering and developing more interpretable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SciQAG is a new way to find answers from lots of scientific papers. It uses special computers called large language models (LLMs) to ask and answer questions about science. These LLMs are trained on 22,743 papers across many fields like biology, physics, and chemistry. The system makes a big dataset with over 188,000 question-answer pairs. This helps other researchers make better AI systems that can understand and reason like humans. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
