Larimar: Large Language Models with Episodic Memory Control
Authors: name: Payel Das; name: Subhajit Chaudhury; name: Elliot Nelson; name: Igor Melnyk; name: Sarath Swaminathan; name: Sihui Dai; name: Aurélie Lozano; name: Georgios Kollias; name: Vijil Chenthamarakshan; name: Jiří; name: Navrátil; name: Soham Dan; name: Pin-Yu Chen
Published: 2024-03-18 16:01:42

Abstract: 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.