Summary of Badge: Badminton Report Generation and Evaluation with Llm, by Shang-hsuan Chiang et al.
BADGE: BADminton report Generation and Evaluation with LLM
by Shang-Hsuan Chiang, Lin-Wei Chao, Kuang-Da Wang, Chih-Chuan Wang, Wen-Chih Peng
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces the BADGE framework, a novel approach for automating badminton report generation and evaluation using Large Language Models (LLMs). The method consists of two phases: Report Generation, where the LLM processes data to produce a detailed match report, and Report Evaluation, which assesses the report’s quality. Various input data types, In-Context Learning (ICL), and LLMs were tested, with GPT-4 performing best using CSV data type and Chain of Thought prompting. Human judges’ scores showed a preference for GPT-4 generated reports. This research lays the groundwork for future advancements in badminton reporting and can be extended to other sports, enhancing their promotion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to automatically generate reports on badminton matches! Researchers created an AI system called BADGE that does just that. They tested different ways of teaching this AI model how to write these reports, and found the best approach. The AI then evaluated its own work and showed it can do a good job writing match reports. This technology has lots of potential to help with reporting on other sports too! |
Keywords
» Artificial intelligence » Gpt » Prompting
