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papersSEP 10 04:00 UTC

New arXiv Study Links LLM Faithfulness to Input Data Plausibility

A newly posted computational linguistics paper examines whether large language models become less faithful to a provided context when the input data seems implausible. The authors analyse how a model's tendency to hallucinate or misinterpret facts varies with the plausibility of what it is given, a question with direct consequences for retrieval-augmented generation and data-to-text systems. The work seeks to clarify when models follow supplied evidence versus falling back on their own priors.

arXiv data-to-text hallucination input-plausibility llm-faithfulness retrieval-augmented generation

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.CLDo LLMs Make More Mistakes If They Do Not Believe the Input Data? ↗SEP 10 04:00 UTC