Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy

Autor:	Tjandra, Benedict Aaron, Razzak, Muhammed, Kossen, Jannik, Handa, Kunal, Gal, Yarin
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Large Language Models (LLMs) are known to hallucinate, whereby they generate plausible but inaccurate text. This phenomenon poses significant risks in critical applications, such as medicine or law, necessitating robust hallucination mitigation strategies. While recent works have proposed fine-tuning methods to teach LLMs to abstain from answering questions beyond their knowledge or capabilities, these methods rely on the existence of ground-truth labels or are limited to short-form responses. To address these limitations, we propose fine-tuning using semantic entropy, an uncertainty measure derived from introspection into the model which does not require external labels. We demonstrate that our approach 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. Comment: Accepted to NeurIPS Safe Generative AI Workshop 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.17234 Zobrazit plný text záznamu View this record from Arxiv