MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory

TL;DR

MemLLM introduces a structured read-write memory module to enhance LLMs, enabling better memorization, updating, and interpretability, thereby improving performance on knowledge-intensive tasks.

Contribution

This paper presents MemLLM, a novel explicit memory augmentation method that allows dynamic interaction, improving LLMs' factual accuracy and interpretability.

Findings

Enhanced performance on knowledge tasks

Improved interpretability of LLMs

Better handling of rare and updated facts

Abstract

While current large language models (LLMs) perform well on many knowledge-related tasks, they are limited by relying on their parameters as an implicit storage mechanism. As a result, they struggle with memorizing rare events and with updating their memory as facts change over time. In addition, the uninterpretable nature of parametric memory makes it challenging to prevent hallucination. Model editing and augmenting LLMs with parameters specialized for memory are only partial solutions. In this paper, we introduce MemLLM, a novel method of enhancing LLMs by integrating a structured and explicit read-and-write memory module. MemLLM tackles the aforementioned challenges by enabling dynamic interaction with the memory and improving the LLM's capabilities in using stored knowledge. Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular. We see MemLLM as an important step towards making LLMs more grounded and factual through memory augmentation. The project repository is publicly available at https://github.com/amodaresi/MemLLM