Tianhang Zhang

Oct 29, 2024 | amazon.science | Xiangkun Hu |Dongyu Ru |Tianhang Zhang |Zheng Zhang

Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents REFCHECKER, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In REFCHECKER, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.