March 14, 2024, 4:48 a.m. | Bangzheng Li, Ben Zhou, Fei Wang, Xingyu Fu, Dan Roth, Muhao Chen

cs.CL updates on

arXiv:2311.09702v2 Announce Type: replace
Abstract: Despite the recent advancement in large language models (LLMs) and their high performances across numerous benchmarks, recent research has unveiled that LLMs suffer from hallucinations and unfaithful reasoning. This work studies a specific type of hallucination induced by semantic associations. Specifically, we investigate to what extent LLMs take shortcuts from certain keyword/entity biases in the prompt instead of following the correct reasoning path. To quantify this phenomenon, we propose a novel probing method and benchmark …

abstract advancement arxiv benchmarks hallucination hallucinations language language models large language large language models llms performances reasoning research semantic studies type work

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