Summary of Fine-tuning Large Language Models to Appropriately Abstain with Semantic Entropy, by Benedict Aaron Tjandra et al.
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy
by Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen, Kunal Handa, Yarin Gal
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Medium Difficulty summary: Large Language Models (LLMs) are known to hallucinate, generating plausible but inaccurate text. This phenomenon poses significant risks in critical applications like medicine or law, necessitating robust hallucination mitigation strategies. To address these limitations, our proposed fine-tuning method using semantic entropy, an uncertainty measure derived from introspection into the model, matches or outperforms models fine-tuned using prior work and achieves strong performance for both short and long-form generations on a range of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Large Language Models can sometimes make things up that aren’t true. This is a problem because it could lead to mistakes in important areas like medicine or law. To solve this issue, we came up with a new way to fine-tune these models using something called semantic entropy. Our approach works well and performs as good as or even better than previous methods on different types of datasets. |
Keywords
* Artificial intelligence * Fine tuning * Hallucination
