How can LLMs collaborate better on complex tasks?
Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
February 18, 2025
https://arxiv.org/pdf/2502.11098This paper introduces TalkHier, a new framework for coordinating multiple LLMs to work together on complex tasks. It addresses the challenges of disorganized communication and inconsistent refinements in current multi-agent LLM systems.
Key points for LLM-based multi-agent systems:
- Structured Communication: TalkHier uses a structured protocol with specific fields for messages, background information, and intermediate outputs, making communication between LLMs clearer and more efficient.
- Hierarchical Refinement: Instead of a flat structure where all agents give feedback at once, TalkHier uses a hierarchical approach. This allows for better summarization and balancing of feedback, leading to less bias and more accurate results.
- Independent Agent Memory: Each LLM has its own memory, allowing it to retain and use information from past interactions, enhancing consistency and efficiency.
- Superior Performance: TalkHier outperforms existing methods on several benchmarks, including question answering and ad text generation, showcasing its effectiveness in various tasks.
- Generalizability: The framework is designed to be modular and adaptable, making it potentially applicable to a broader range of tasks beyond the tested benchmarks.