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.

通过对主流大型语言模型在各种体育任务上进行广泛评估，我们揭示了自然语言处理中体育理解的关键挑战，并提出了一个基于现有体育数据集的全面概述的新基准，以帮助确定未来的研究优先事项。

体育智能：通过问题回答从文本到视频评估语言模型对体育理解能力

Sports Intelligence: Assessing the Sports Understanding Capabilities of  Language Models through Question Answering from Text to Video

A deep understanding of sports, a field rich in strategic and dynamic
content, is crucial for advancing Natural Language Processing (NLP). This holds
particular significance in the context of evaluating and advancing Large
Language Models (LLMs), given the existing gap in specialized benchmarks. To
bridge this gap, we introduce SportQA, a novel benchmark specifically designed
for evaluating LLMs in the context of sports understanding. SportQA encompasses
over 70,000 multiple-choice questions across three distinct difficulty levels,
each targeting different aspects of sports knowledge from basic historical
facts to intricate, scenario-based reasoning tasks. We conducted a thorough
evaluation of prevalent LLMs, mainly utilizing few-shot learning paradigms
supplemented by chain-of-thought (CoT) prompting. Our results reveal that while
LLMs exhibit competent performance in basic sports knowledge, they struggle
with more complex, scenario-based sports reasoning, lagging behind human
expertise. The introduction of SportQA marks a significant step forward in NLP,
offering a tool for assessing and enhancing sports understanding in LLMs.

SportQA 是一个专门设计用于评估大型语言模型在体育理解方面的新型基准，通过超过 70,000 个多项选择题，涵盖了三个不同难度级别，从基本历史事实到复杂的场景推理任务，综合使用少样本学习和思维链提示对主流大型语言模型进行了全面评估，结果表明，虽然大型语言模型在基本体育知识方面表现出色，但在更复杂的场景推理中表现不佳，与人类专业知识相比较有落后，引入 SportQA 为自然语言处理领域迈出了重要一步，为评估和提升大型语言模型的体育理解能力提供了一个工具。