Can LLMs mimic human collaboration?
Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models
September 24, 2024
https://arxiv.org/pdf/2409.13753This research investigates if multiple smaller LLMs working together can solve problems more effectively than individual LLMs, mimicking human collaboration.
Key takeaways for LLM-based multi-agent systems:
- Open-source LLMs: Using open-source LLMs like Mistral7B-instruct on powerful hardware like MSOE's Rosie supercomputer offers flexibility and eliminates usage restrictions compared to proprietary LLMs.
- Agent-environment interaction: The research explores different environments (simulated apartment, coding task) where agents interact via a central moderator, handling actions and observations.
- Observation and memory: Agents employ a system of "observations" to retain information beyond the LLM's context window, crucial for long-term task solving.
- Challenges and limitations: The research highlights challenges in coordinating agents, ensuring they adhere to constraints, and managing complex programmatic tasks. Single LLMs sometimes outperformed multi-agent setups.
- Promising direction: Mediated communication through a moderator shows potential for collaborative problem-solving in complex, simulated environments, especially as open-source LLMs improve in context retention.