FT-LLM: 축구 산업의 데이터 분석 지원을 위한 Retrieval 증강 생성 기반의 언어 모델 프레임워크 개발

한승헌; 강민혁; 강필성; 황우현

doi:10.7232/JKIIE.2024.50.2.097

주제분류

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저널정보

대한산업공학회
대한산업공학회지 학술저널
대한산업공학회지 제50권 제2호
2024.4 97 - 108 (12page)
DOI : 10.7232/JKIIE.2024.50.2.097

저자정보

한승헌 (고려대학교)
강민혁 (고려대학교)
강필성 (고려대학교)
황우현 (고려대학교)

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초록·키워드

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In the football industry, data analysis is gradually gaining more recognition as a key tool for developing match and club management strategies and analyzing players. Consequently, large language models (LLMs), with the ability to offer customized responses to user inquiries, could be a valuable data analysis resource. In this paper, we propose FT-LLM, a framework that enhances the language generation capability of language models by incorporating a partially cleansed, football-related documents and a retrieval. Compared to existing RAG methodologies, FT-LLM provides more suitable answer for football related queries by effectively incorporating external documents with the help of Query Refinement and Confidence Check modules. By employing a chain of thought-based prompting strategy to prevent hallucination, language models can convey more relevant and reliable football related insights to users. As a result, FT-LLM has the potential to assist data-driven decision making and operations in the football industry by formulating a match strategy or advising player acquisition.

#Natural Language Processing #Retrieval Augmented Generation #Football Data Analysis