重新定义大语言模型中的“幻觉”：迈向基于心理学框架的虚假信息缓解
Redefining "Hallucination" in LLMs: Towards a psychology-informed framework for mitigating misinformation
AI总结：
本文质疑大语言模型“幻觉”术语的适用性，提出基于认知偏差的心理学分类法，以细粒度理解并借鉴人类内部解决机制，制定策略缓解虚假信息，提升模型可靠性。
摘要：
近年来，大语言模型（LLMs）变得极为流行，例如ChatGPT已被超过十亿用户使用。尽管这些模型展现出卓越的语言理解和逻辑能力，但一个显著挑战以“幻觉”的形式浮现。这一现象导致LLMs以自信的方式输出虚假信息，在如此庞大的用户群体下可能带来毁灭性后果。然而，我们质疑“幻觉”一词在LLMs中的适用性，提出一种基于认知偏差和其他心理现象的心理学分类法。我们的方法提供了对这一现象更细粒度的理解，从而允许有针对性的解决方案。通过借鉴人类如何内部解决类似挑战的见解，我们旨在制定策略以缓解LLM幻觉。这种跨学科方法寻求超越传统术语，为提升LLM可靠性提供细致入微的理解和可行的改进路径。
英文摘要：
In recent years, large language models (LLMs) have become incredibly popular, with ChatGPT for example being used by over a billion users. While these models exhibit remarkable language understanding and logical prowess, a notable challenge surfaces in the form of "hallucinations." This phenomenon results in LLMs outputting misinformation in a confident manner, which can lead to devastating consequences with such a large user base. However, we question the appropriateness of the term "hallucination" in LLMs, proposing a psychological taxonomy based on cognitive biases and other psychological phenomena. Our approach offers a more fine-grained understanding of this phenomenon, allowing for targeted solutions. By leveraging insights from how humans internally resolve similar challenges, we aim to develop strategies to mitigate LLM hallucinations. This interdisciplinary approach seeks to move beyond conventional terminology, providing a nuanced understanding and actionable pathways for improvement in LLM reliability.
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。
