How can I improve LLM agent feedback?
HOW TO CORRECTLY DO SEMANTIC BACKPROPAGATION ON LANGUAGE-BASED AGENTIC SYSTEMS
This paper introduces "semantic backpropagation," a method for optimizing language-based multi-agent AI systems represented as computational graphs. It addresses the challenge of effectively assigning feedback to individual agent components based on overall system output. Semantic backpropagation generalizes the concept of gradients from numerical optimization to semantic feedback, allowing for the use of LLMs in both forward execution of agents and optimization of the system. This allows for automatic optimization of the multi-agent system, reducing manual effort. Key to the method is incorporating "neighborhood" information during feedback propagation—considering the interplay between connected agents rather than treating them in isolation, a limitation of prior methods like TextGrad. This is demonstrated to improve performance in question-answering tasks on various benchmarks, including BIG-Bench Hard and GSM8K. The method's minimalist nature and effectiveness in optimizing complex agentic systems are emphasized.