MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents

• URL: http://arxiv.org/abs/2408.14033v2
• Date: Mon, 2 Sep 2024 05:55:06 GMT
• Title: MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents
• Authors: Ruochen Li, Teerth Patel, Qingyun Wang, Xinya Du,
• Abstract summary: We present a new systematic framework, autonomous Machine Learning Research with large language models (MLR-Copilot) It is designed to enhance machine learning research productivity through the automatic generation and implementation of research ideas using Large Language Model (LLM) agents. We evaluate our framework on five machine learning research tasks and the experimental results show the framework's potential to facilitate the research progress and innovations.
• Score: 10.86017322488788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning research, crucial for technological advancements and innovation, often faces significant challenges due to its inherent complexity, slow pace of experimentation, and the necessity for specialized expertise. Motivated by this, we present a new systematic framework, autonomous Machine Learning Research with large language models (MLR-Copilot), designed to enhance machine learning research productivity through the automatic generation and implementation of research ideas using Large Language Model (LLM) agents. The framework consists of three phases: research idea generation, experiment implementation, and implementation execution. First, existing research papers are used to generate hypotheses and experimental plans vis IdeaAgent powered by LLMs. Next, the implementation generation phase translates these plans into executables with ExperimentAgent. This phase leverages retrieved prototype code and optionally retrieves candidate models and data. Finally, the execution phase, also managed by ExperimentAgent, involves running experiments with mechanisms for human feedback and iterative debugging to enhance the likelihood of achieving executable research outcomes. We evaluate our framework on five machine learning research tasks and the experimental results show the framework's potential to facilitate the research progress and innovations.

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