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ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study titled 'ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models' addresses the reliability of LLMs in mathematical reasoning, an area previously underexplored. Researchers developed the ReliableMath dataset, which includes both solvable and high-quality unsolvable problems. Experiments conducted on various LLMs revealed that while larger models show improved reliability when prompted correctly, they still fail to recognize unsolvable problems and often produce inaccurate responses. Smaller models, however, show little progress even with reliable prompts, prompting the proposal of an alignment strategy to enhance their reliability. This research underscores the critical need for advancements in LLM reliability, particularly in mathematical reasoning, to ensure their effective application in real-world scenarios.
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