Papers › MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, Yuqing Yang
The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML tasks (e.g., AutoML) are often time-consuming and hard to understand for human developers. In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches. In this paper, we aim to bridge the gap between machine intelligence and human knowledge by introducing a novel framework, which leverages the state-of-the-art large language models to develop ML solutions for novel tasks. We showcase the possibility of extending the capability of LLMs to comprehend structured inputs and perform thorough reasoning for solving novel ML tasks. And we find that, after some dedicated design, the LLM can (i) observe from the existing experiences of ML tasks and (ii) reason effectively to deliver promising results for new tasks. The solution generated can be used directly to achieve high levels of competitiveness. Examples and code available at https://github.com/microsoft/CoML.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | DSEval-LeetCode | CoML | Pass Rate | 42.5 | #4 of 5 | Archive leaderboard | report |
| Code Generation | DSEval-LeetCode | CoML | w/o Intact | 42.5 | #4 of 5 | Archive leaderboard | report |
| Code Generation | DSEval-LeetCode | CoML | w/o PE | 62.5 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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