April 2, 2024, 7:51 p.m. | Ankit Satpute, Noah Giessing, Andre Greiner-Petter, Moritz Schubotz, Olaf Teschke, Akiko Aizawa, Bela Gipp

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.00344v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities in various natural language tasks, often achieving performances that surpass those of humans. Despite these advancements, the domain of mathematics presents a distinctive challenge, primarily due to its specialized structure and the precision it demands. In this study, we adopted a two-step approach for investigating the proficiency of LLMs in answering mathematical questions. First, we employ the most effective LLMs, as identified by their performance on math …

arxiv cs.ai cs.cl cs.ir language language models large language large language models llms master math stack stack exchange type

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