Summary of Bayesian Calibration Of Win Rate Estimation with Llm Evaluators, by Yicheng Gao et al.
Bayesian Calibration of Win Rate Estimation with LLM Evaluators
by Yicheng Gao, Gonghan Xu, Zhe Wang, Arman Cohan
First submitted to arxiv on: 7 Nov 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 explores ways to accurately evaluate the quality of text generated by large language models (LLMs). Currently, LLMs can be used to assess the quality of other LLM-generated texts, but this approach has a built-in bias that makes results unreliable. To address this issue, the authors propose two methods: Bayesian Win Rate Sampling (BWRS) and Bayesian Dawid-Skene. These methods use Bayesian inference to improve the accuracy of win rate estimation. The paper evaluates these methods on six datasets covering different tasks like story generation, summarization, and instruction following. The results show that both methods are effective in improving the accuracy of win rate estimation using LLMs as evaluators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making sure we can trust computers to judge how good or bad text written by other computers is. Right now, these computers can be used to compare and rank different texts, but this system has a problem that makes it not very reliable. To fix this issue, the scientists came up with two new ways to make the computer evaluation more accurate. They tested these methods on six sets of text data for tasks like writing stories, summarizing information, and following instructions. The results show that both new methods work well in making the computer judgment more trustworthy. |
Keywords
» Artificial intelligence » Bayesian inference » Summarization
