AI Research
LLM Hallucination Detection via Low-rank Multilinear Decomposition
A decomposition-based approach targets fluent but factually incorrect LLM responses
Large language models (LLMs) frequently generate fluent yet factually incorrect responses, raising concerns about their reliability.
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ArXiv CS.CL
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ArXiv CS.CL
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Fluent language can make incorrect answers seem trustworthy, so detecting hallucinations is important to assessing LLM reliability. The work applies low-rank multilinear decomposition to this detection problem.
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