Memory Sharing for Large Language Model based Agents
Hang Gao Yongfeng Zhang
In the realm of artificial intelligence, the adaptation of Large Language
Model (LLM)-based agents to execute tasks via natural language prompts
represents a significant advancement, notably eliminating the need for explicit
retraining or fine tuning for fixed-answer tasks such as common sense questions
and yes/no queries. However, the application of In-context Learning to
open-ended challenges, such as poetry creation, reveals substantial limitations
due to the comprehensiveness of the provided examples and agent's ability to
understand the content expressed in the problem, leading to outputs that often
diverge significantly from expected results. Addressing this gap, our study
introduces the Memory-Sharing (MS) framework for LLM multi-agents, which
utilizes a real-time memory storage and retrieval system to enhance the
In-context Learning process. Each "memory" within this system captures both the
posed query and the corresponding real-time response from an LLM-based agent,
aggregating these memories from a broad spectrum of similar agents to enrich
the memory pool shared by all agents. This framework not only aids agents in
identifying the most relevant examples for specific tasks but also evaluates
the potential utility of their memories for future applications by other
agents. Empirical validation across three distinct domains involving
specialized functions of agents demonstrates that the MS framework
significantly improve the agent's performance regrading the open-ended
questions. Furthermore, we also discuss what type of memory pool and what
retrieval strategy in MS can better help agents, offering a future develop
direction of MS. The code and data are available at:
https://github.com/GHupppp/MemorySharingLLM