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.

通过基于认知偏见和其他心理现象的心理分类学，我们质疑将 “幻觉” 一词应用于大型语言模型，并利用人类内部解决类似挑战的见解来开发缓解 LLMs 幻觉的策略，为提高 LLM 的可靠性提供细致精确的理解和可操作的途径。