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

cs.CL updates on arXiv.org arxiv.org

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 cs.ai cs.cl hallucination hallucinations language language models large language large language models llms performances reasoning research semantic studies type work

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Senior Research Engineer/Specialist - Motor Mechanical Design

@ GKN Aerospace | Bristol, GB

Research Engineer (Motor Mechanical Design)

@ GKN Aerospace | Bristol, GB

Senior Research Engineer (Electromagnetic Design)

@ GKN Aerospace | Bristol, GB

Associate Research Engineer Clubs | Titleist

@ Acushnet Company | Carlsbad, CA, United States