Summary of Olaph: Improving Factuality in Biomedical Long-form Question Answering, by Minbyul Jeong et al.
OLAPH: Improving Factuality in Biomedical Long-form Question Answering
by Minbyul Jeong, Hyeon Hwang, Chanwoong Yoon, Taewhoo Lee, Jaewoo Kang
First submitted to arxiv on: 21 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 This paper introduces MedLFQA, a benchmark dataset for evaluating the factual claims generated by large language models (LLMs) in the biomedical domain. The authors propose OLAPH, a framework that uses cost-effective automatic evaluations to construct a synthetic preference set and answer questions in a preferred manner. They train LLMs using this framework and show that it leads to significant performance improvements in factuality, even on evaluation metrics not used during training. Specifically, they demonstrate that a 7B LLM trained with OLAPH can provide long answers comparable to medical experts’ answers in terms of factuality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn to give good answers about medicine. It creates a special set of questions and answers (MedLFQA) to test how well these machines can tell the truth. The authors also make a new way (OLAPH) to check if what the machines say is true or not. They find that by using this method, they can train machines to give better answers. This could help doctors and other experts work with machines to get accurate information. |
