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Can Large Language Models do Analytical Reasoning?
March 8, 2024, 5:47 a.m. | Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
cs.CL updates on arXiv.org arxiv.org
Abstract: This paper explores the cutting-edge Large Language Model with analytical reasoning on sports. Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games. Our major discoveries are in two folds. Firstly, we find among all the models we employed, GPT-4 stands out in effectiveness, followed by Claude-2.1, with GPT-3.5, Gemini-Pro, and Llama-2-70b lagging behind. Specifically, we compare three …
abstract arxiv count cs.ai cs.cl discoveries edge games language language model language models large language large language model large language models major nba nfl paper reasoning sports tasks team type
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