How can LLMs learn and adapt without parameter updates?
COMPOSITE LEARNING UNITS: GENERALIZED LEARNING BEYOND PARAMETER UPDATES TO TRANSFORM LLMS INTO ADAPTIVE REASONERS
October 11, 2024
https://arxiv.org/pdf/2410.08037This paper introduces Composite Learning Units (CLUs), an AI architecture designed to enable continuous learning and adaptation in AI systems, particularly those using Large Language Models (LLMs).
Here's how it relates to LLM-based multi-agent systems:
- Continuous Learning: CLUs move beyond static training, using feedback loops to refine their knowledge base (divided into general and task-specific knowledge) through interactions with tasks, similar to how multi-agent systems can learn by interacting with each other and their environment.
- Knowledge Management: CLUs employ a Knowledge Management Unit (KMU) that dynamically stores, retrieves, and prunes knowledge based on feedback, making it relevant for LLM-based systems that need to efficiently manage and utilize large knowledge bases.
- Modularity: The CLU architecture, with its separate agents for prompt generation, reasoning, and feedback analysis, is designed to be modular. This aligns with multi-agent systems where individual agents with specialized roles collaborate to solve complex tasks.
- Beyond Traditional LLMs: While the paper showcases CLU with LLMs, its principles are applicable to any reasoning system. This is key for multi-agent systems where diverse agents (including symbolic reasoners) could leverage the CLU framework for continuous learning.