3 citations · 5 across the 2 of their papers we have counts for
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cs.CL2024
Measuring and Reducing LLM Hallucination without Gold-Standard Answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton +3
LLM hallucination, i.e. generating factually incorrect yet seemingly convincing answers, is currently a major threat to the trustworthiness and reliability of LLMs. The first step…
cs.CL2024★ 2 cited
Human-Instruction-Free LLM Self-Alignment with Limited Samples
Hongyi Guo, Yuanshun Yao, Wei Shen +4
Aligning large language models (LLMs) with human values is a vital task for LLM practitioners. Current alignment techniques have several limitations: (1) requiring a large amount o…
cs.CL2023
Large Language Model Unlearning
Yuanshun Yao, Xiaojun Xu, Yang Liu
We study how to perform unlearning, i.e. forgetting undesirable misbehaviors, on large language models (LLMs). We show at least three scenarios of aligning LLMs with human preferen…