Addressing the issue of hallucinations in large language models (LLMs) is a critical challenge. As the cognitive mechanisms of hallucination have been related to memory, here we explore hallucination for LLM that is enabled with explicit memory mechanisms. We empirically demonstrate that by simply scaling the readout vector that constrains generation in a memory-augmented LLM decoder, hallucination mitigation can be achieved in a training-free manner. Our method is geometry-inspired and outperforms a state-of-the-art LLM editing method on the task of generation of Wikipedia-like biography entries both in terms of generation quality and runtime complexity.

翻译：解决大型语言模型（LLMs）中的幻觉问题是一项关键挑战。由于幻觉的认知机制与记忆相关，本文探讨了配备显式记忆机制的LLM中的幻觉现象。我们通过实验证明，在具有记忆增强功能的LLM解码器中，仅需缩放约束生成的读出向量，即可在不进行训练的情况下实现幻觉缓解。该方法受几何原理启发，在生成维基百科式传记条目的任务中，无论在生成质量还是运行时间复杂度方面均优于当前最先进的LLM编辑方法。