[1B3-GS-2] Machine learning: Generative model

[1B3-GS-2-01] Function Calling for Structured Responses in Large Language Models for Automated Classification of Non-Functional Requirements in Information Systems

〇Kazuhiro Mukaida¹, Seiji Fukui², Takeshi Nagaoka², Takayuki Kitagawa², Shinpei Ogata¹, Kozo Okano¹ (1. Shinshu University, 2. Toshiba Corporation)

Keywords:ChatGPT, Function Calling, Fine tuning, Text classification, Non-Functional Requirements

We focus on non-functional requirements, which are often overlooked in requirement definitions, and propose a method that allows even those without extensive expertise to efficiently extract and classify non-functional requirements from requirement specifications. Previously, the authors have experimented with classification using models that incorporate pre-trained Transformer models such as BERT and GPT-2. Recently, with the proliferation of tools like ChatGPT, it has become possible to perform classifications solely through prompt interactions. In this study, we explore the capabilities of ChatGPT's Function calling feature, focusing on its potential to yield superior results compared to responses generated solely from prompts and traditional methods. We leverage Function calling to obtain structured data for classification. Additionally, we assess the impact of fine-tuning on ChatGPT and its combined effect. As a result, we were able to significantly shorten the entire process of model creation and learning, achieving accuracy equal to or greater than traditional methods.