Summary of Just Rephrase It! Uncertainty Estimation in Closed-source Language Models Via Multiple Rephrased Queries, by Adam Yang et al.
Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries
by Adam Yang, Chen Chen, Konstantinos Pitas
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 focuses on improving the accuracy of large language models (LLMs) that are not open-source, but widely used by the public. These models often provide confident answers without indicating their uncertainty, which can lead to false information being spread. To address this issue, the authors propose a method for estimating the uncertainty of these closed-source LLMs. They achieve this by asking multiple rephrased questions and comparing the answers to determine the uncertainty. The paper presents rules for rephrasing queries that are easy to use in practice and provides a theoretical framework explaining why this approach works. The results show significant improvements in the calibration of uncertainty estimates compared to the baseline, making it useful for critical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make language models more honest. When you ask these models questions, they usually give answers with confidence, but sometimes they can be wrong. This can be a problem because people might believe what the model says and spread false information. The authors want to fix this by coming up with a way to figure out when the model is unsure or maybe even wrong. They do this by asking the same question again, but in different words. If the answers are similar, that means the model is likely correct. But if the answers are very different, then the model might be unsure or wrong. The paper shows that this method works well and can help us use language models more wisely. |
