HalluVerse25：面向大语言模型幻觉的细粒度多语言基准数据集
HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations
AI总结：
本文提出多语言细粒度幻觉基准数据集HalluVerse25，覆盖英、阿、土三语，通过LLM注入与人工标注构建，并评估多个LLM的幻觉检测能力。
摘要：
大语言模型（LLMs）在各类场景中的应用日益广泛，但仍容易生成非事实性内容，通常被称为“幻觉”。文献将幻觉划分为多种类型，包括实体级、关系级和句子级幻觉。然而，现有的幻觉数据集往往无法在多语言环境中捕捉细粒度幻觉。在本工作中，我们提出了HalluVerse25，这是一个多语言LLM幻觉数据集，对英语、阿拉伯语和土耳其语中的细粒度幻觉进行分类。我们的数据集构建流程使用一个LLM将幻觉注入事实性传记句子中，随后经过严格的人工标注过程以确保数据质量。我们在HalluVerse25上评估了多个LLM，为专有模型在不同语境下检测LLM生成幻觉的表现提供了有价值的见解。
英文摘要：
Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations". The literature categorizes hallucinations into several types, including entity-level, relation-level, and sentence-level hallucinations. However, existing hallucination datasets often fail to capture fine-grained hallucinations in multilingual settings. In this work, we introduce HalluVerse25, a multilingual LLM hallucination dataset that categorizes fine-grained hallucinations in English, Arabic, and Turkish. Our dataset construction pipeline uses an LLM to inject hallucinations into factual biographical sentences, followed by a rigorous human annotation process to ensure data quality. We evaluate several LLMs on HalluVerse25, providing valuable insights into how proprietary models perform in detecting LLM-generated hallucinations across different contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
