The Gray Zone of Faithfulness: Taming Ambiguity in Unfaithfulness Detection

• URL: http://arxiv.org/abs/2510.21118v2
• Date: Mon, 27 Oct 2025 02:50:12 GMT
• Title: The Gray Zone of Faithfulness: Taming Ambiguity in Unfaithfulness Detection
• Authors: Qiang Ding, Lvzhou Luo, Yixuan Cao, Ping Luo,
• Abstract summary: Existing benchmarks suffer from annotation ambiguity due to the ill-defined boundary of permissible external knowledge.<n>We propose a novel faithfulness annotation framework, which introduces an intermediate category, Out-Dependent.<n>Using this framework, we construct VeriGray -- a new unfaithfulness detection benchmark in summarization.
• Score: 27.25270218197248
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Ensuring that Large Language Models (LLMs) generate summaries faithful to a given source document is essential for real-world applications. While prior research has explored LLM faithfulness, existing benchmarks suffer from annotation ambiguity, primarily due to the ill-defined boundary of permissible external knowledge in generated outputs. For instance, common sense is often incorporated into responses and labeled as "faithful", yet the acceptable extent of such knowledge remains unspecified, leading to inconsistent annotations. To address this issue, we propose a novel faithfulness annotation framework, which introduces an intermediate category, Out-Dependent, to classify cases where external knowledge is required for verification. Using this framework, we construct VeriGray (Verification with the Gray Zone) -- a new unfaithfulness detection benchmark in summarization. Statistics reveal that even SOTA LLMs, such as GPT-5, exhibit hallucinations ($\sim 6\%$ of sentences) in summarization tasks. Moreover, a substantial proportion ($\sim 8\%$ on average of models) of generated sentences fall into the Out-Dependent category, underscoring the importance of resolving annotation ambiguity in unfaithfulness detection benchmarks. Experiments demonstrate that our benchmark poses significant challenges to multiple baseline methods, indicating considerable room for future improvement.

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