Summary of Rethinking Uncertainty Estimation in Natural Language Generation, by Lukas Aichberger et al.
Rethinking Uncertainty Estimation in Natural Language Generation
by Lukas Aichberger, Kajetan Schweighofer, Sepp Hochreiter
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 tackles the critical issue of evaluating the trustworthiness of text generated by Large Language Models (LLMs). The authors highlight that current approaches to uncertainty estimation are impractical at scale due to computational expenses. They delve into the theoretical foundations of existing methods, exploring new directions for efficiency enhancements. By leveraging proper scoring rules and the negative log-likelihood of the most likely output sequence, they propose G-NLL as an alternative uncertainty measure that can be obtained using a single output sequence generated by greedy decoding. This approach achieves state-of-the-art performance across various LLMs and tasks, laying the groundwork for efficient and reliable uncertainty estimation in natural language generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure the text produced by super smart computers (Large Language Models) is trustworthy. Right now, it’s hard to figure out if what they say is true or not. The authors want to make it easier to check how certain these computers are about what they’re saying. They looked at how people do this now and found a way to do it that doesn’t take as long. This new method works well for different types of computer models and tasks, which could help us use these computers in more ways. |
Keywords
» Artificial intelligence » Log likelihood
