定位大模型输出中的幻觉位置，提升多语言检测精度
UCSC at SemEval-2025 Task 3: Context, Models and Prompt Optimization for Automated Hallucination Detection in LLM Output
- 三步流程：检索上下文、识别错误内容、映射到输出片段
- 自动优化提示词，多语言平均位置排名榜首
- 适合关注幻觉检测与可解释性研究的开发者
幻觉是大语言模型在回答知识密集型问题时面临的主要挑战。随着大模型广泛应用，不仅需要判断是否存在幻觉，还需精确定位其在输出中的具体位置。SemEval 2025 Task 3 Mu-SHROOM：多语言幻觉与相关过生成错误共享任务，正是为此方向的最新尝试。本文介绍了加州大学圣克鲁斯分校（UCSC）对Mu-SHROOM任务的系统提交。我们提出一个框架：先检索相关上下文，再识别答案中的虚假内容，最后将错误信息回映到大模型输出的具体文本片段中。该流程通过自动提示词优化进一步增强。我们的系统在所有语言上的平均位置排名位列第一，取得最高整体性能。代码与实验结果已公开。
原文摘要 · Abstract (English)
Hallucinations pose a significant challenge for large language models when answering knowledge-intensive queries. As LLMs become more widely adopted, it is crucial not only to detect if hallucinations occur but also to pinpoint exactly where in the LLM output they occur. SemEval 2025 Task 3, Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, is a recent effort in this direction. This paper describes the UCSC system submission to the shared Mu-SHROOM task. We introduce a framework that first retrieves relevant context, next identifies false content from the answer, and finally maps them back to spans in the LLM output. The process is further enhanced by automatically optimizing prompts. Our system achieves the highest overall performance, ranking #1 in average position across all languages. We release our code and experiment results.
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