Summary of Language Models Can Evaluate Themselves Via Probability Discrepancy, by Tingyu Xia et al.
Language Models can Evaluate Themselves via Probability Discrepancy
by Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang, Chang Zhou
First submitted to arxiv on: 17 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 In this paper, the authors explore how Large Language Models (LLMs) respond to queries when they are more skilled or less skilled. They find that better LLMs exhibit a more even probability distribution in their answers. Building on this idea, the researchers propose a new self-evaluation method called ProbDiff for assessing the effectiveness of various LLMs. This approach eliminates the need for an additional evaluation model and instead uses the LLMs being tested to compute the probability discrepancy between initial responses and revised versions. The authors test ProbDiff across different LLMs, NLG tasks (such as translation and summarization), and benchmarks like AlignBench, MT-Bench, and AlpacaEval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are special kinds of computer programs that can understand and respond to human language. In this study, scientists looked at how these models answer questions when they’re good or not so good. They found that better models give answers with a more even chance of being correct. To help figure out which model is best, the researchers came up with a new way to measure how well models do. This method uses the same models being tested to compare their answers and see how different they are from each other. The study showed that this new method works just as well as using a special external model to test the LLMs. |
Keywords
» Artificial intelligence » Probability » Summarization » Translation
