том 38 издание 17 страницы 19724-19731

MemoryBank: Enhancing Large Language Models with Long-Term Memory

Тип публикацииJournal Article
Дата публикации2024-03-24
core rank A*
SJR0.133
CiteScore2
Impact factor
ISSN21595399, 23743468
Psychiatry and Mental health
Neuropsychology and Physiological Psychology
Краткое описание

Large Language Models (LLMs) have drastically reshaped our interactions with artificial intelligence (AI) systems, showcasing impressive performance across an extensive array of tasks. Despite this, a notable hindrance remains—the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems, psychological counseling, and secretarial assistance. Recognizing the necessity for long-term memory, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user's personality over time by synthesizing information from previous interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory. This mechanism permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a more human-like memory mechanism and enriched user experience. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models such as ChatGLM. To validate MemoryBank's effectiveness, we exemplify its application through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialog data, SiliconFriend displays heightened empathy and discernment in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as multiple users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.

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ГОСТ |
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Zhong W. et al. MemoryBank: Enhancing Large Language Models with Long-Term Memory // Proceedings of the AAAI Conference on Artificial Intelligence. 2024. Vol. 38. No. 17. pp. 19724-19731.
ГОСТ со всеми авторами (до 50) Скопировать
Zhong W., Guo L., Gao Q., Ye He, Wang Y. MemoryBank: Enhancing Large Language Models with Long-Term Memory // Proceedings of the AAAI Conference on Artificial Intelligence. 2024. Vol. 38. No. 17. pp. 19724-19731.
RIS |
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TY - JOUR
DO - 10.1609/aaai.v38i17.29946
UR - https://ojs.aaai.org/index.php/AAAI/article/view/29946
TI - MemoryBank: Enhancing Large Language Models with Long-Term Memory
T2 - Proceedings of the AAAI Conference on Artificial Intelligence
AU - Zhong, Wanjun
AU - Guo, Lianghong
AU - Gao, Qiqi
AU - Ye He
AU - Wang, Yanlin
PY - 2024
DA - 2024/03/24
PB - Association for the Advancement of Artificial Intelligence (AAAI)
SP - 19724-19731
IS - 17
VL - 38
SN - 2159-5399
SN - 2374-3468
ER -
BibTex |
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BibTex (до 50 авторов) Скопировать
@article{2024_Zhong,
author = {Wanjun Zhong and Lianghong Guo and Qiqi Gao and Ye He and Yanlin Wang},
title = {MemoryBank: Enhancing Large Language Models with Long-Term Memory},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2024},
volume = {38},
publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
month = {mar},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/29946},
number = {17},
pages = {19724--19731},
doi = {10.1609/aaai.v38i17.29946}
}
MLA
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Zhong, Wanjun, et al. “MemoryBank: Enhancing Large Language Models with Long-Term Memory.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 17, Mar. 2024, pp. 19724-19731. https://ojs.aaai.org/index.php/AAAI/article/view/29946.
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