Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video
Zhengbang Yang, Haotian Xia, Jingxi Li, Zezhi Chen, Zhuangdi Zhu, Weining Shen
TL;DR
A new benchmark based on a comprehensive overview of existing sports datasets is proposed based on a comprehensive overview of existing sports datasets and extensive error analysis is provided which can help identify future research priorities in this field.
Abstract
Understanding sports is crucial for the advancement of Natural Language Processing (NLP) due to its intricate and dynamic nature. Reasoning over complex sports scenarios has posed significant challenges to current NLP technologies which require advanced cognitive capabilities. Toward addressing the limitations of existing benchmarks on sports understanding in the NLP field, we extensively evaluated mainstream large language models for various sports tasks. Our evaluation spans from simple queries on basic rules and historical facts to complex, context-specific reasoning, leveraging strategies from zero-shot to few-shot learning, and chain-of-thought techniques. In addition to unimodal analysis, we further assessed the sports reasoning capabilities of mainstream video language models to bridge the gap in multimodal sports understanding benchmarking. Our findings highlighted the critical challenges of sports understanding for NLP. We proposed a new benchmark based on a comprehensive overview of existing sports datasets and provided extensive error analysis which we hope can help identify future research priorities in this field.