Year: 2025
Type: conference-paper
Abstract: In applications such as dialogue systems, personalized recommendations, and personal assistants, large language models (LLMs) need to retain and utilize historical information over the long term to provide more accurate and consistent responses.Although long-term memory capability is crucial, recent studies have not thoroughly investigated the memory performance of large language models in longterm tasks.To address this gap, we introduce the Long-term Chronological Conversations (LOCCO) dataset and conduct a quantitative evaluation of the long-term memory capabilities of large language models.Experimental results demonstrate that large language models can retain past interaction information to a certain extent, but their memory decays over time.While rehearsal strategies can enhance memory persistence, excessive rehearsal is not an effective memory strategy for large models, unlike in smaller models.Additionally, the models exhibit memory preferences across different categories of information.Our study not only provides a new framework and dataset for evaluating the long-term memory capabilities of large language models but also offers important references for future enhancements of their memory persistence.
Authors: Zixi Jia, Qinghua Liu, Hexiao Li, Yuyan Chen, Jiqiang Liu
Institutions: Northeastern University, Fudan University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888195