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Large language models (LLMs) have significantly advanced the field of naturallanguage processing (NLP) through their extensive parameters and comprehensivedata utilization. However, existing LLMs lack a dedicated memory unit, limitingtheir ability to explicitly store and retrieve knowledge for various tasks. Inthis paper, we propose RET-LLM a novel framework that equips LLMs with ageneral write-read memory unit, allowing them to extract, store, and recallknowledge from the text as needed for task performance. Inspired by Davidsoniansemantics theory, we extract and save knowledge in the form of triplets. Thememory unit is designed to be scalable, aggregatable, updatable, andinterpretable. Through qualitative evaluations, we demonstrate the superiorityof our proposed framework over baseline approaches in question answering tasks.Moreover, our framework exhibits robust performance in handling temporal-basedquestion answering tasks, showcasing its ability to effectively managetime-dependent information.

RET-LLM: Towards a General Read-Write Memory for Large Language Models