Year: 2024
Type: preprint
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 8-10x 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, information leakage prevention, and input context length generalization with Larimar and show their effectiveness. Our code is available at https://github.com/IBM/larimar
Source: arXiv (Cornell University)
Authors: Das, Payel, Chaudhury, Subhajit, Nelson, Elliot, Melnyk, Igor, Swaminathan, Sarath, Dai, Sihui, Lozano, Aurélie, Kollias, Georgios, Chenthamarakshan, Vijil, Jiří, Navrátil, Dan, Soham, Chen, Pin-Yu
Institution: Princeton University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4392971664