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Despite their impressive generative capabilities, LLMs are hindered byfact-conflicting hallucinations in real-world applications. The accurateidentification of hallucinations in texts generated by LLMs, especially incomplex inferential scenarios, is a relatively unexplored area. To address thisgap, we present FactCHD, a dedicated benchmark designed for the detection offact-conflicting hallucinations from LLMs. FactCHD features a diverse datasetthat spans various factuality patterns, including vanilla, multi-hop,comparison, and set operation. A distinctive element of FactCHD is itsintegration of fact-based evidence chains, significantly enhancing the depth ofevaluating the detectors' explanations. Experiments on different LLMs exposethe shortcomings of current approaches in detecting factual errors accurately.Furthermore, we introduce Truth-Triangulator that synthesizes reflectiveconsiderations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aimingto yield more credible detection through the amalgamation of predictive resultsand evidence. The benchmark dataset is available athttps://github.com/zjunlp/FactCHD.

FactCHD: Benchmarking Fact-Conflicting Hallucination Detection