Automation of algorithm selection and execution code generation by large language models for AI agents

Abstract

In recent years, there has been much development of LLM-based AI agents. For example, in the sales process, AI agents are expected to improve business efficiency by selecting business partners and preparing documents based on the results of business negotiations. Meanwhile, the need for AI agents is increasing in the manufacturing industry. Data handled in the manufacturing industry is becoming increasingly large and complex, and in order to analyze data and obtain useful results, it is necessary not only to select the appropriate method for the problem, but also to perform coding work including preprocessing and parameter adjustment. However, users are not always familiar with analysis methods, and in some cases, it takes time to select a method or create an execution code. Therefore, this paper proposes a method to solve these issues by utilizing LLM. Specifically, the following two points will be discussed. (1) Algorithm selection utilizing information about the data to be analyzed (2) Prompt correction by feedback of execution resultsExperiments with artificial data confirm that fine tuning BERT allows the selection of an appropriate factor estimation algorithm with high accuracy. We also confirmed that correcting the prompts based on error statements during code execution can result in executable code.