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Larimar: Large Language Models with Episodic Memory Control

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arxiv 2403.11901 v4 pith:CD5C5NNF submitted 2024-03-18 cs.LG cs.AI

Larimar: Large Language Models with Episodic Memory Control

Payel Das , Subhajit Chaudhury , Elliot Nelson , Igor Melnyk , Sarath Swaminathan , Sihui Dai , Aur\'elie Lozano , Georgios Kollias
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Vijil Chenthamarakshan Ji\v{r}\'i Navr\'atil Soham Dan Pin-Yu Chen
This is my paper
classification cs.LG cs.AI
keywords larimarmemoryarchitectureeditingepisodicfactknowledgelanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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

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Cited by 4 Pith papers

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  4. A Survey of Context Engineering for Large Language Models

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    The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle...