Summary of Uncertainty Estimation Of Large Language Models in Medical Question Answering, by Jiaxin Wu et al.
Uncertainty Estimation of Large Language Models in Medical Question Answering
by Jiaxin Wu, Yizhou Yu, Hong-Yu Zhou
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 benchmarks uncertainty estimation (UE) methods for Large Language Models (LLMs) in medical question-answering tasks. Current UE approaches perform poorly in this domain, highlighting the challenge of detecting hallucinations in LLMs. The study proposes Two-phase Verification, a probability-free approach that generates step-by-step explanations and formulates verification questions to check factual claims. This method achieves better accuracy and stability across various datasets and model sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical question-answering using Large Language Models (LLMs) is promising but has a risk of producing incorrect information. To solve this problem, researchers need a way to measure how sure the model is about its answers. The paper tests different methods for doing this and finds that they don’t work well in medical applications. The study then presents a new method called Two-phase Verification that generates explanations and asks questions to check if the answers are correct. This approach does better than other methods and gets more accurate as the size of the model increases. |
Keywords
» Artificial intelligence » Probability » Question answering
