analiza aiqbench
PyraMathBench addresses a critical gap in LLM evaluation by introducing a large-scale benchmark that specifically isolates failures in numerical computation and abstract reasoning. The study demonstrates that current models struggle with these tasks due to weak tool integration, proposing SOLVE and IRPO methods that leverage efficient tool calls to significantly improve mathematical synergy. This work provides a necessary diagnostic framework for developers aiming to enhance the reliability of LLMs in quantitative applications.
PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models
arXiv:2606.03858v1 Announce Type: new Abstract: Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few benchmarks evaluate LLMs by integrating numerical processing and mathematical reasoning, hindering the interpretability of failures in math tasks. We introduce PyraMathBench, a comprehensive hierarchical benchmark with 32,505 questions derived from 7,404 math word problems, spanning 4 key cognitive aspects, 14 subcategories, and 2 modalities. Experiments reveal that LLMs' performance is severely compromised by inadequate numerical computation and weak handling of abstract numerical questions. To address this, we propose the Smart Optimization & Learning-based VErsatile module (SOLVE) and Interactive Relative Policy Optimization (IRPO), which enhance LLMs' numerical-mathematical synergy via efficient tool calls (fuzzy matching and low-quality call rejection). Comparative experiments show Qwen-2.5 achieves a 5.0 score improvement with SOLVE and IRPO training.
Abstract:Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few benchmarks evaluate LLMs by integrating numerical processing and mathematical reasoning, hindering the interpretability of failures in math tasks. We introduce PyraMathBench, a comprehensive hierarchical benchmark with 32,505 questions derived from 7,404 math word problems, spanning 4 key cognitive aspects, 14 subcategories, and 2 modalities. Experiments reveal that LLMs' performance is severely compromised by inadequate numerical computation and weak handling of abstract numerical questions. To address this, we propose the Smart Optimization & Learning-based VErsatile module (SOLVE) and Interactive Relative Policy Optimization (IRPO), which enhance LLMs' numerical-mathematical synergy via efficient tool calls (fuzzy matching and low-quality call rejection). Comparative experiments show Qwen-2.5 achieves a 5.0 score improvement with SOLVE and IRPO training.
Artificial Intelligence (cs.AI)
arXiv:2606.03858 [cs.AI]
(or arXiv:2606.03858v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.03858 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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