Can LLMs better simulate power systems with multi-agent feedback?
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
This paper introduces a feedback-driven multi-agent framework to improve the ability of Large Language Models (LLMs) to perform power system simulations. Current LLMs struggle with these tasks due to limited domain-specific knowledge, weak reasoning capabilities, and difficulty handling simulation parameters. The framework addresses these issues with: 1) an enhanced retrieval augmented generation (RAG) system for accessing relevant information, 2) an improved reasoning module using chain-of-thought and few-shot prompting, and 3) an environmental action module that allows the LLM to interact with the simulation environment, receive feedback, and correct errors. This multi-agent approach allows the LLM to learn and adapt, achieving significantly higher success rates on complex simulation tasks compared to baseline LLMs and even outperforming LLMs with standard RAG implementations. This framework demonstrates the potential of multi-agent systems to enhance LLM performance in specialized domains.