Summary of Efficient Medical Question Answering with Knowledge-augmented Question Generation, by Julien Khlaut et al.
Efficient Medical Question Answering with Knowledge-Augmented Question Generation
by Julien Khlaut, Corentin Dancette, Elodie Ferreres, Alaedine Bennani, Paul Hérent, Pierre Manceron
First submitted to arxiv on: 23 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 paper introduces a method to improve the proficiency of small language models in the medical domain by fine-tuning them on a corpus of medical textbooks. The approach involves two steps: first, using GPT-4 to generate questions similar to the downstream task and prompting them with textbook knowledge; then, fine-tuning the model on these generated questions. Additionally, the paper presents ECN-QA, a novel medical question answering dataset containing progressive questions composed of related sequential questions. The study shows that this training strategy is effective on this dataset, highlighting the potential of small language models in the medical domain when appropriately fine-tuned. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make smaller language models better at understanding and answering medical questions. To do this, they use a two-step process: first, they train a bigger model (GPT-4) to ask similar questions based on what’s learned from textbooks; then, they fine-tune the smaller model using these generated questions. They also introduce a new dataset of medical questions that get progressively harder, showing how their method works well with this type of data. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Prompting » Question answering
