Large Language Models Synergize with Automated Machine Learning

Recently, code generation driven by large language models (LLMs) has become increasingly popular. However, automatically generating code for machine learning (ML) tasks still poses significant challenges. This paper explores the limits of program synthesis for ML by combining LLMs and automated machine learning (autoML). Specifically, our goal is to fully automate the code generation process for the entire ML workflow, from data preparation to modeling and post-processing, utilizing only textual descriptions of the ML tasks. To manage the length and diversity of ML programs, we propose to break each ML program into smaller, manageable parts. Each part is generated separately by the LLM, with careful consideration of their compatibilities. To implement the approach, we design a testing technique for ML programs. Furthermore, our approach enables integration with autoML. In our approach, autoML serves to numerically assess and optimize the ML programs generated by LLMs. LLMs, in turn, help to bridge the gap between theoretical, algorithm-centered autoML and practical autoML applications. This mutual enhancement underscores the synergy between LLMs and autoML in program synthesis for ML. In experiments across various ML tasks, our method outperforms existing methods in 10 out of 12 tasks for generating ML programs. In addition, autoML significantly improves the performance of the generated ML programs. In the experiments, our method, Text-to-ML, achieves fully automated synthesis of the entire ML pipeline based solely on textual descriptions of the ML tasks.