故意诱发型精神异常（PIP）：将大型语言模型中的幻觉视为想象
Purposefully Induced Psychosis (PIP): Embracing Hallucination as Imagination in Large Language Models
AI总结：
针对大型语言模型幻觉被视为错误的现状，提出故意诱发型精神异常（PIP）方法，将幻觉重新定义为计算想象来源，通过微调放大其创造性幻觉，为创意任务提供新途径并探讨相关伦理与协作问题。
摘要：
大型语言模型（LLMs）中的幻觉通常被视为错误——偏离事实准确性的输出。但在创意或探索性场景中，这些“错误”可能代表意想不到的创新途径。我们提出故意诱发型精神异常（PIP）这一新颖方法，为推测性小说、互动叙事和混合现实模拟等想象任务放大LLM幻觉。借鉴赫尔曼·梅尔维尔《白鲸记》中皮普的“疯狂”揭示深刻洞见的情节，我们将幻觉重新定义为计算想象的来源而非缺陷。我们的方法微调LLMs以鼓励推测性、隐喻性和超现实输出——在事实准确性并非首要目标时有用的幻觉。受戏剧和舞台魔术的共识性错觉启发，PIP将这些创造性失误置于用户自愿暂停怀疑的场景中，从而将“错误”转化为新思维方式的催化剂。我们讨论潜在应用、确保用户同意的设计原则、初步观察以及对更广泛AI伦理和人机协作的启示。
英文摘要：
Hallucinations in Large Language Models (LLMs) are widely regarded as errors - outputs that deviate from factual accuracy. However, in creative or exploratory contexts, these "mistakes" may represent unexpected avenues for innovation. We introduce Purposefully Induced Psychosis (PIP), a novel approach that amplifies LLM hallucinations for imaginative tasks such as speculative fiction, interactive storytelling, and mixed-reality simulations. Drawing on Herman Melville's Moby-Dick, where Pip's "madness" reveals profound insight, we reframe hallucinations as a source of computational imagination rather than a flaw. Our method fine-tunes LLMs to encourage speculative, metaphorical, and surreal outputs - hallucinations that are useful when factual accuracy is not the chief objective. Inspired by the consensual illusions of theater and stage magic, PIP situates these creative missteps in contexts where users willingly suspend disbelief, thereby transforming "errors" into catalysts for new ways of thinking. We discuss potential applications, design principles for ensuring user consent, preliminary observations, and implications for broader AI ethics and human-AI collaboration.
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。
