2023/04/28 by Lei Zhang, Yuge Zhang, Zhang, Lei +7 · 1 voice · 17 citations
Computer Science · Engineering · Materials Science · #Artificial intelligence #Automation #Bridge (graph theory) #Code (set theory) #Computer science #Data science #Engineering #Field (mathematics) #Human–computer interaction #Machine Learning and Data Classification #Machine Learning in Materials Science #Machine learning #Programming language #Software engineering #Topic Modeling #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2304.14979
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/04/28 · openalex created_date 2023/05/02 · openalex updated_date 2026/07/28
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.