重新思考幻觉：正确性、一致性与提示多样性
Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity
AI总结：
本文提出提示多样性框架，揭示LLM幻觉评估中一致性的重要性，指出现有检测与缓解技术的局限性。
摘要：
大型语言模型（LLMs）已知会通过生成虚假或误导性的输出来
英文摘要：
Large language models (LLMs) are known to "hallucinate" by generating false or misleading outputs. Hallucinations pose various harms, from erosion of trust to widespread misinformation. Existing hallucination evaluation, however, focuses only on correctness and often overlooks consistency, necessary to distinguish and address these harms. To bridge this gap, we introduce prompt multiplicity, a framework for quantifying consistency in LLM evaluations. Our analysis reveals significant multiplicity (over 50% inconsistency in benchmarks like Med-HALT), suggesting that hallucination-related harms have been severely misunderstood. Furthermore, we study the role of consistency in hallucination detection and mitigation. We find that: (a) detection techniques detect consistency, not correctness, and (b) mitigation techniques like RAG, while beneficial, can introduce additional inconsistencies. By integrating prompt multiplicity into hallucination evaluation, we provide an improved framework of potential harms and uncover critical limitations in current detection and mitigation strategies.
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。
