title: Larimar: Large Language Models with Episodic Memory Control
summary: 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.
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paper_title: Larimar: Large Language Models with Episodic Memory Control
paper_summary: 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.
paper_ai_keywords: Larimar brain-inspired architecture distributed episodic memory one-shot updates sequential editing selective fact forgetting input context length generalization