Improving negative rejection ability in language models: A review of fine-tuned LLMs, RAG, and RAFT

Abstract Large Language Models (LLMs) excel in text understanding and generation but struggle to reject irrelevant, ambiguous, or misleading queries, termed negative rejection, impacting reliability in high-stakes contexts. This paper reviews negative rejection, analyzing three approaches: fine-tune...

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Bibliografiset tiedot

Päätekijät:	Li Bowen, Zhong Zhuoqing, Zhang Hengyu, Sun Xubin, Baha Ihnaini
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Springer 2025-12-01
Sarja:	Journal of King Saud University: Computer and Information Sciences
Aiheet:	Negative rejection LLM safety Fine-tuning RAG RAFT Hallucination mitigation
Linkit:	https://doi.org/10.1007/s44443-025-00334-6
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