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.

翻译：大型语言模型（LLM）凭借其庞大参数量和全面数据利用能力，显著推动了自然语言处理（NLP）领域的发展。然而，现有LLM缺乏专用记忆单元，限制了其在各类任务中显式存储和检索知识的能力。本文提出RET-LLM这一新型框架，为LLM配备通用读写记忆单元，使其能够根据任务需求从文本中提取、存储和调用知识。受Davidsonian语义理论的启发，我们以三元组形式提取和存储知识。该记忆单元具有可扩展、可聚合、可更新及可解释的特性。通过定性评估，我们证明了所提出框架在问答任务中相较于基线方法的优越性。此外，该框架在处理基于时序的问答任务时表现稳健，展示了其有效管理时间依赖性信息的能力。