Summary of Medexqa: Medical Question Answering Benchmark with Multiple Explanations, by Yunsoo Kim et al.
MedExQA: Medical Question Answering Benchmark with Multiple Explanations
by Yunsoo Kim, Jinge Wu, Yusuf Abdulle, Honghan Wu
First submitted to arxiv on: 10 Jun 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 A novel benchmark for evaluating large language models’ (LLMs) understanding of medical knowledge through explanations is introduced. The MedExQA benchmark addresses a major gap in current medical QA benchmarks by incorporating multiple explanations for each question-answer pair, assessing LLMs’ ability to generate nuanced medical explanations. This work highlights the importance of explainability in medical LLMs and proposes an effective methodology for evaluating models beyond classification accuracy. The results show that generation evaluation with multiple explanations aligns better with human assessment, highlighting an opportunity for a more robust automated comprehension assessment for LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well big language models understand medical information by asking them questions and checking their answers. It makes a special dataset for this task that includes many different kinds of medical specialties and multiple ways to explain the answers. This helps us figure out if these big language models are really good at understanding medical information, or just good at giving simple answers. |
Keywords
» Artificial intelligence » Classification
