https://arxiv.org/abs/2410.05229 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2410.05229 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2410.05229 (cs) [Submitted on 7 Oct 2024] Title:GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models Authors:Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel , Samy Bengio, Mehrdad Farajtabar View a PDF of the paper titled GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models, by Iman Mirzadeh and 5 other authors View PDF HTML (experimental) Abstract:Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of this http URL findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning. Comments: preprint Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2410.05229 [cs.LG] (or arXiv:2410.05229v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2410.05229 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Seyed Iman Mirzadeh [view email] [v1] Mon, 7 Oct 2024 17:36:37 UTC (5,949 KB) Full-text links: Access Paper: View a PDF of the paper titled GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models, by Iman Mirzadeh and 5 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.LG < prev | next > new | recent | 2024-10 Change to browse by: cs cs.AI References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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