Construction of Automated Machine Learning(AutoML) Framework Based on Large LanguageModels

As for automatic machine learning (AutoML),which can simplify the model design and adjustment process,continues to develop with the evolution of machine learningtechnology, and has become one of the factors promotingintelligent applications. Nevertheless, current AutoMLframeworks still have a lot of limitations in many aspects whenaddressing more complex problems, particularly for settingswhere large-scale language models (LLMs) are utilized forautomated learning. In this paper, we present a large-scalelanguage model based automated machine learning framework.First of all, the framework integrates the existing automaticfeature engineering and hyperparameter optimizationtechnologies, and further utilizes the intelligent assistance of LLMmodel in the process of model generation, optimization and modelinference to improve the accuracy and efficiency of the wholeautomation process. Its innovation is the deep fusion of large-scalelanguage models and traditional AutoML working processes, andto automatically generate and fine-tune many machine learningmodels in multi-modal data and complex task scenarios based ontheir powerful contextual understanding and generationcapabilities to realize more accurate and efficient modeling.Experimental results demonstrate that proposed frameworkimproves the model's adaptive capacity and inference efficiency.