MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Conference of the European Chapter of the Association for Computational Linguistics(2023)
摘要
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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