Can cheaper LLMs automate ML tasks?
BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks
This paper introduces BudgetMLAgent, a multi-agent system designed to automate machine learning tasks cost-effectively using less expensive LLMs. It demonstrates that combining less expensive LLMs like Gemini-Pro with techniques like profiling (assigning distinct roles/personas to agents), cascades (chaining LLMs of increasing cost), retrieval of past actions, and occasional calls to more powerful LLMs (like GPT-4) for expert planning can achieve better performance at a much lower cost than using a single, expensive LLM like GPT-4 alone. The system achieves significant cost reduction (up to 94.2%) while maintaining or even exceeding the performance of single-agent systems in several ML tasks. BudgetMLAgent addresses the economic challenges of using large LLMs by leveraging the strengths of smaller, more affordable LLMs in a cooperative multi-agent framework.