  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.
