MeMo: Memory as a Model
For practitioners needing to update LLMs with timely, domain-specific knowledge, MeMo provides a plug-and-play solution that works with both open and proprietary models without retraining.
MeMo introduces a modular memory framework that encodes new knowledge into a separate model, allowing LLMs to incorporate domain-specific information without retraining. It achieves strong performance on BrowseComp-Plus, NarrativeQA, and MuSiQue benchmarks, outperforming existing methods while avoiding catastrophic forgetting and requiring no access to LLM weights.
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.