Summary of Can Gpt Redefine Medical Understanding? Evaluating Gpt on Biomedical Machine Reading Comprehension, by Shubham Vatsal et al.
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
by Shubham Vatsal, Ayush Singh
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 evaluation of large language models (LLMs) on closed-book biomedical machine reading comprehension (MRC) tasks is presented. The authors test GPT-3 on four MRC benchmarks using conventional prompting techniques and a newly introduced Implicit Retrieval Augmented Generation (RAG) approach, which alleviates the need for vector databases. The results show that the proposed RAG method achieves state-of-the-art performance on two datasets and ranks second on the others. Notably, zero-shot LLMs like GPT outperform supervised models in two benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. Researchers have been testing these models to see how well they can do certain tasks, like reading medical texts. In this study, scientists used a popular model called GPT-3 to test its abilities on four different medical reading comprehension tasks. They tried using some new techniques to help the model work better and found that it did really well! In fact, the model was able to do just as well or even better than models that were specifically trained for this task. |
Keywords
» Artificial intelligence » Gpt » Prompting » Rag » Retrieval augmented generation » Supervised » Zero shot
