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Computer Science > Machine Learning

arXiv:2304.14979 (cs)
[Submitted on 28 Apr 2023]

Title:MLCopilot: Unleashing the Power of Large Language Models in
Solving Machine Learning Tasks

Authors:Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, Yuqing Yang
Download a PDF of the paper titled MLCopilot: Unleashing the Power of
Large Language Models in Solving Machine Learning Tasks, by Lei Zhang
and 4 other authors
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    Abstract: The field of machine learning (ML) has gained
    widespread adoption, leading to a 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 MLCopilot,
    which leverages the state-of-the-art LLMs 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.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2304.14979 [cs.LG]
          (or arXiv:2304.14979v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2304.14979
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yuge Zhang [view email]
[v1] Fri, 28 Apr 2023 17:03:57 UTC (204 KB)
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    Power of Large Language Models in Solving Machine Learning Tasks,
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