Summary of Sportsmetrics: Blending Text and Numerical Data to Understand Information Fusion in Llms, by Yebowen Hu et al.
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
by Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: Large language models (LLMs) have the potential to integrate various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents significant challenges, including processing and cross-referencing entities and numbers, handling data inconsistencies and redundancies, and developing planning capabilities like building a working memory for managing complex data queries. To evaluate the numerical reasoning and information fusion capabilities of LLMs, we introduce four novel tasks centered around sports data analytics, involving detailed game descriptions, adversarial scenarios, and analyzing key statistics in game summaries. We conduct extensive experiments on NBA and NFL games to assess the performance of LLMs on these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about using special computer models called large language models (LLMs) for advanced data analysis. The challenge is combining different types of data, like text and numbers, into one cohesive picture. To test how well LLMs can do this, we created four new tasks focused on sports data analytics. We gave the models detailed descriptions of sports games and then challenged them with unexpected scenarios, like changes to game rules or scrambled narratives. Our experiments used real sports data from the NBA and NFL to see how well the models performed. |
