Larimar: Large Language Models with Episodic Memory Control
Payel Das Subhajit Chaudhury Elliot Nelson Igor Melnyk Sarath Swaminathan Sihui Dai Aurélie Lozano Georgios Kollias Vijil Chenthamarakshan Jiří Navrátil Soham Dan Pin-Yu Chen
Efficient and accurate updating of knowledge stored in Large Language Models
(LLMs) is one of the most pressing research challenges today. This paper
presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with
a distributed episodic memory. Larimar's memory allows for dynamic, one-shot
updates of knowledge without the need for computationally expensive re-training
or fine-tuning. Experimental results on multiple fact editing benchmarks
demonstrate that Larimar attains accuracy comparable to most competitive
baselines, even in the challenging sequential editing setup, but also excels in
speed - yielding speed-ups of 4-10x depending on the base LLM - as well as
flexibility due to the proposed architecture being simple, LLM-agnostic, and
hence general. We further provide mechanisms for selective fact forgetting and
input context length generalization with Larimar and show their effectiveness.