MeMo: Memory as a Model

TL;DR

MeMo is a modular memory framework that enables large language models to incorporate new knowledge efficiently without retraining, capturing complex relationships and maintaining robustness.

Contribution

It introduces a plug-and-play memory module that enhances LLMs with new knowledge while preserving their original parameters and avoiding catastrophic forgetting.

Findings

MeMo outperforms existing methods on three benchmarks.

It effectively captures complex cross-document relationships.

It maintains robustness to retrieval noise.

Abstract

Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications require timely, domain-specific information, motivating the need for efficient mechanisms to incorporate new knowledge. In this paper, we introduce MeMo (Memory as a Model), a modular framework that encodes new knowledge into a dedicated memory model while keeping the LLM parameters unchanged. Compared to existing methods, MeMo offers several advantages: (a) it captures complex cross-document relationships, (b) it is robust to retrieval noise, (c) it avoids catastrophic forgetting in the LLM, (d) it does not require access to the LLM's weights or output logits, enabling plug-and-play integration with both open and proprietary closed-source LLMs, and (e) its retrieval cost is independent of corpus size at inference time. Our experimental results on three benchmarks, BrowseComp-Plus, NarrativeQA, and MuSiQue, show that MeMo achieves strong performance compared to existing methods across diverse settings.