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【第42期】SELA：使用MCTS增强LLM
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：SELA: Tree-Search Enhanced LLM Agents for Automated Machine LearningSummary
The source explores a new method for automated machine learning called Tree-Search Enhanced LLM Agents (SELA). SELA uses a large language model (LLM) to suggest potential machine learning strategies, then employs Monte Carlo Tree Search (MCTS) to efficiently explore these options, iteratively refining its approach based on experimental results. This process mimics the nuanced problem-solving approach of human experts and consistently outperforms other AutoML systems and LLM-based agents, particularly in its ability to adapt to diverse datasets and task requirements.
原文链接：https://arxiv.org/abs/2410.17238
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：SELA: Tree-Search Enhanced LLM Agents for Automated Machine LearningSummary
The source explores a new method for automated machine learning called Tree-Search Enhanced LLM Agents (SELA). SELA uses a large language model (LLM) to suggest potential machine learning strategies, then employs Monte Carlo Tree Search (MCTS) to efficiently explore these options, iteratively refining its approach based on experimental results. This process mimics the nuanced problem-solving approach of human experts and consistently outperforms other AutoML systems and LLM-based agents, particularly in its ability to adapt to diverse datasets and task requirements.
原文链接：https://arxiv.org/abs/2410.17238