Summary of Word-sequence Entropy: Towards Uncertainty Estimation in Free-form Medical Question Answering Applications and Beyond, by Zhiyuan Wang et al.
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
by Zhiyuan Wang, Jinhao Duan, Chenxi Yuan, Qingyu Chen, Tianlong Chen, Yue Zhang, Ren Wang, Xiaoshuang Shi, Kaidi Xu
First submitted to arxiv on: 22 Feb 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 This paper introduces Word-Sequence Entropy (WSE), a novel method for estimating uncertainty in open-ended medical question-answering tasks. WSE calibrates uncertainty at both the word and sequence levels, considering semantic relevance. The authors compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in uncertainty quantification under two standard criteria for correctness evaluation. Additionally, employing responses with lower uncertainty identified by WSE as final answers improves model accuracy on the COVID-QA dataset without additional task-specific fine-tuning or architectural modifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to measure how sure computers are when answering open-ended medical questions. Right now, there isn’t a good way to do this, which can lead to biased answers. The authors introduce a method called Word-Sequence Entropy (WSE) that looks at both individual words and sequences of words to figure out how uncertain the computer is. They tested WSE on several medical question-answering datasets and found that it works better than other methods. This new way of estimating uncertainty can help make computers more reliable in healthcare engineering. |
Keywords
* Artificial intelligence * Fine tuning * Question answering
