Evaluating LLMs' Assessment of Mixed-Context Hallucination Through the Lens of Summarization

• URL: http://arxiv.org/abs/2503.01670v1
• Date: Mon, 03 Mar 2025 15:42:57 GMT
• Title: Evaluating LLMs' Assessment of Mixed-Context Hallucination Through the Lens of Summarization
• Authors: Siya Qi, Rui Cao, Yulan He, Zheng Yuan,
• Abstract summary: Large language models (LLMs) have emerged as a widely adopted approach for text quality evaluation, including hallucination evaluation.<n>In this study, we use summarization as a representative task to evaluate LLMs' capability in detecting mixed-context hallucinations.
• Score: 23.739195769774103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid development of large language models (LLMs), LLM-as-a-judge has emerged as a widely adopted approach for text quality evaluation, including hallucination evaluation. While previous studies have focused exclusively on single-context evaluation (e.g., discourse faithfulness or world factuality), real-world hallucinations typically involve mixed contexts, which remains inadequately evaluated. In this study, we use summarization as a representative task to comprehensively evaluate LLMs' capability in detecting mixed-context hallucinations, specifically distinguishing between factual and non-factual hallucinations. Through extensive experiments across direct generation and retrieval-based models of varying scales, our main observations are: (1) LLMs' intrinsic knowledge introduces inherent biases in hallucination evaluation; (2) These biases particularly impact the detection of factual hallucinations, yielding a significant performance bottleneck; (3) The fundamental challenge lies in effective knowledge utilization, balancing between LLMs' intrinsic knowledge and external context for accurate mixed-context hallucination evaluation.

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