RELIC: Investigating Large Language Model Responses using Self-Consistency

arXiv:2311.16842v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are notorious for blending fact with fiction and generating non-factual content, known as hallucinations. To address this challenge, we propose an interactive system that helps users gain insight into the reliability of the generated text. Our approach is based on the idea that the self-consistency of multiple samples generated by the same LLM relates to its confidence in individual claims in the generated texts. Using this idea, we design RELIC, an …

abstract arxiv challenge cs.cl cs.hc fiction generated hallucinations insight interactive language language model language models large language large language model large language models llms reliability responses text type