大语言模型幻觉：全面调查
Large Language Models Hallucination: A Comprehensive Survey
AI总结：
本文全面调查大语言模型中的幻觉现象，分析其成因、检测与缓解方法，探讨现有评估指标及未来研究方向。
摘要：
大语言模型（LLMs）已彻底改变自然语言处理，但在多样任务中表现出色的同时，其生成内容常出现事实错误或缺乏外部证据支持，即幻觉现象。本文综述了LLMs中幻觉的研究，重点探讨其成因、检测与缓解方法，分析幻觉在关键自然语言生成任务中的产生机制，介绍检测与缓解策略的分类体系，评估现有方法的优劣，并回顾现有评估基准和指标，最后提出关键开放挑战与未来研究方向，为开发更真实可信的LLMs奠定基础。
英文摘要：
Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks. However, their impressive fluency often comes at the cost of producing false or fabricated information, a phenomenon known as hallucination. Hallucination refers to the generation of content by an LLM that is fluent and syntactically correct but factually inaccurate or unsupported by external evidence. Hallucinations undermine the reliability and trustworthiness of LLMs, especially in domains requiring factual accuracy. This survey provides a comprehensive review of research on hallucination in LLMs, with a focus on causes, detection, and mitigation. We first present a taxonomy of hallucination types and analyze their root causes across the entire LLM development lifecycle, from data collection and architecture design to inference. We further examine how hallucinations emerge in key natural language generation tasks. Building on this foundation, we introduce a structured taxonomy of detection approaches and another taxonomy of mitigation strategies. We also analyze the strengths and limitations of current detection and mitigation approaches and review existing evaluation benchmarks and metrics used to quantify LLMs hallucinations. Finally, we outline key open challenges and promising directions for future research, providing a foundation for the development of more truthful and trustworthy LLMs.
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。
