Summary of Conu: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees, by Zhiyuan Wang et al.
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees
by Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Hengtao Shen, Xiaofeng Zhu
First submitted to arxiv on: 29 Jun 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 study tackles uncertainty quantification (UQ) in natural language generation (NLG) tasks, specifically addressing the challenge posed by large language models (LLMs). It proposes a novel uncertainty measure based on self-consistency theory and integrates it with conformal prediction (CP) to create a conformal uncertainty criterion. The authors demonstrate that this approach outperforms prior state-of-the-art methods and achieve strict control over correctness coverage rates using 7 LLMs on 4 free-form NLG datasets, covering general-purpose and medical scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertainty in language generation is a big problem. This study tries to solve it by finding new ways to measure how certain we are about the results. They use a special technique called conformal prediction that can turn any idea of uncertainty into a set of predictions that are guaranteed to be correct. The researchers come up with a new way to calculate uncertainty based on self-consistency theory and combine it with this conformal prediction method. They test their approach using 7 large language models on 4 different datasets and find that it works better than other methods. |
