幻觉的统一定义：是世界模型的问题，笨蛋！
A Unified Definition of Hallucination: It's The World Model, Stupid!
AI总结：
本文提出幻觉的统一定义，即用户可观察到的错误内部世界建模，并连接至HalluWorld基准测试，以区分真实幻觉与规划或奖励错误。
摘要：
尽管自语言模型诞生以来已有无数缓解尝试，但即使在当今最前沿的LLM中，幻觉仍然是一个持续存在的问题。这是为什么？我们回顾了现有的幻觉定义，并将它们整合为一个统一的定义，其中先前的定义被包含在内。我们认为，幻觉可以通过将其简单地定义为不准确的（内部）世界建模来统一，其形式是用户可观察到的。例如，陈述与知识库相矛盾的事实，或生成与来源相矛盾的摘要。通过改变参考世界模型和冲突策略，我们的框架统一了先前的定义。我们认为，这种统一观点是有用的，因为它迫使评估澄清其假定的参考“世界”，区分真实幻觉与规划或奖励错误，并为跨基准比较和缓解策略讨论提供共同语言。基于这一定义，我们还将我们的框架连接到HalluWorld，这是一个补充基准，它实例化了完全指定的参考世界模型，用于压力测试模型幻觉。
英文摘要：
Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. We argue that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we also connect our framework to HalluWorld, a complementary benchmark that instantiates fully specified reference world models for stress-testing model hallucinations.
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。
