Summary of Towards Leveraging Large Language Models For Automated Medical Q&a Evaluation, by Jack Krolik et al.
Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation
by Jack Krolik, Herprit Mahal, Feroz Ahmad, Gaurav Trivedi, Bahador Saket
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper investigates the possibility of utilizing Large Language Models (LLMs) to automate response evaluation in medical Question and Answer (Q&A) systems, a crucial aspect of Natural Language Processing. By leveraging LLMs, the study aims to reduce the reliance on manual human evaluation by medical professionals, which is time-consuming and costly. The authors use questions derived from patient data to assess the performance of LLMs in replicating human evaluations. While the results show promising outcomes, further research is required to tackle more complex or specific queries that were beyond the scope of this initial investigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using special language models called Large Language Models (LLMs) to help evaluate answers in medical question-and-answer systems. These systems are important for natural language processing. Traditionally, people have had to manually check the quality of these answers, but this takes up a lot of time and money from doctors. The researchers want to see if LLMs can do this job instead. They used questions based on patient data to test the models’ ability to match human evaluations. So far, the results look promising, but more work is needed to tackle harder or more specific questions. |
Keywords
» Artificial intelligence » Natural language processing
