BADGE: BADminton report Generation and Evaluation with LLM
<|reference_start|>BADGE: BADminton report Generation and Evaluation with LLM: 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 https://github.com/AndyChiangSH/BADGE.<|reference_end|>
@article{chiang2024badge:,
    title={BADGE: BADminton report Generation and Evaluation with LLM},
    author={Shang-Hsuan Chiang, Lin-Wei Chao, Kuang-Da Wang, Chih-Chuan Wang,
  Wen-Chih Peng},
    journal={arXiv preprint arXiv:2406.18116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18116},
    primaryClass={cs.CL cs.AI cs.HC}
}