In this paper, we present HalluSearch, a multilingual pipeline designed to detect fabricated text spans in Large Language Model (LLM) outputs. Developed as part of Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, HalluSearch couples retrieval-augmented verification with fine-grained factual splitting to identify and localize hallucinations in fourteen different languages. Empirical evaluations show that HalluSearch performs competitively, placing fourth in both English (within the top ten percent) and Czech. While the system's retrieval-based strategy generally proves robust, it faces challenges in languages with limited online coverage, underscoring the need for further research to ensure consistent hallucination detection across diverse linguistic contexts.

本研究针对大型语言模型输出中生成的虚假文本片段进行检测的缺口，提出了一种多语言的HalluSearch管道。通过结合增强检索验证和细粒度事实分解，该论文实现了对十四种语言中的幻觉的识别和定位，并通过实验证明了其具有竞争力的表现，尤其在英语和捷克语中的有效性。

HalluSearch在SemEval-2025任务3中的应用：增强搜索的RAG管道用于幻觉检测