  Large language models (LLMs) have significantly advanced the field of natural
language processing (NLP) through their extensive parameters and comprehensive
data utilization. However, existing LLMs lack a dedicated memory unit, limiting
their ability to explicitly store and retrieve knowledge for various tasks. In
this paper, we propose RET-LLM a novel framework that equips LLMs with a
general write-read memory unit, allowing them to extract, store, and recall
knowledge from the text as needed for task performance. Inspired by Davidsonian
semantics theory, we extract and save knowledge in the form of triplets. The
memory unit is designed to be scalable, aggregatable, updatable, and
interpretable. Through qualitative evaluations, we demonstrate the superiority
of our proposed framework over baseline approaches in question answering tasks.
Moreover, our framework exhibits robust performance in handling temporal-based
question answering tasks, showcasing its ability to effectively manage
time-dependent information.
