生成约束缩放能缓解幻觉
Generation Constraint Scaling Can Mitigate Hallucination
AI总结：
本文针对具备显式记忆机制的LLM幻觉问题，提出一种受几何启发的无训练方法，通过缩放记忆增强解码器中的读取向量来约束生成，在生成质量与运行复杂度上均优于现有LLM编辑方法。
摘要：
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，本文探索了具备显式记忆机制的LLM的幻觉问题。我们实证表明，仅需缩放记忆增强LLM解码器中约束生成的读取向量，即可在无训练的情况下实现幻觉缓解。我们的方法受几何启发，在生成类似维基百科传记条目的任务上，无论在生成质量还是运行时复杂度方面，均优于最先进的LLM编辑方法。
英文摘要：
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.
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。
