Badminton enjoys widespread popularity, and reports on matches generally
include details such as player names, game scores, and ball types, providing
audiences with a comprehensive view of the games. However, writing these
reports can be a time-consuming task. This challenge led us to explore whether
a Large Language Model (LLM) could automate the generation and evaluation of
badminton reports. We introduce a novel framework named BADGE, designed for
this purpose using LLM. Our method consists of two main phases: Report
Generation and Report Evaluation. Initially, badminton-related data is
processed by the LLM, which then generates a detailed report of the match. We
tested different Input Data Types, In-Context Learning (ICL), and LLM, finding
that GPT-4 performs best when using CSV data type and the Chain of Thought
prompting. Following report generation, the LLM evaluates and scores the
reports to assess their quality. Our comparisons between the scores evaluated
by GPT-4 and human judges show a tendency to prefer GPT-4 generated reports.
Since the application of LLM in badminton reporting remains largely unexplored,
our research serves as a foundational step for future advancements in this
area. Moreover, our method can be extended to other sports games, thereby
enhancing sports promotion. For more details, please refer to
this https URL

我们引入一个名为 BADGE 的新框架，利用大型语言模型自动生成和评估羽毛球比赛报告，并测试了不同输入数据类型和学习方法。比较评估结果表明 GPT-4 生成的报告更受青睐，为羽毛球报道中应用语言模型提供了基础，并可扩展至其他体育项目，促进体育推广。