Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models
Abstract: We introduce Mathador-LM, a new benchmark for evaluating the mathematical reasoning on large language models (LLMs), combining ruleset interpretation, planning, and problem-solving. This benchmark is inspired by the Mathador game, where the objective is to reach a target number using basic arithmetic operations on a given set of base numbers, following a simple set of rules.
We show that, across leading LLMs, we obtain stable average performance while generating benchmark instances dynamically, following a target difficulty level.
Thus, our benchmark alleviates concerns about test-set leakage into training data, an issue that often undermines popular benchmarks.
Additionally, we conduct a comprehensive evaluation of both open and closed-source state-of-the-art LLMs on Mathador-LM. Our findings reveal that contemporary models struggle with Mathador-LM, scoring significantly lower than average 5th graders. This stands in stark contrast to their strong performance on popular mathematical reasoning benchmarks.
Paper Type: Short
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, automatic creation and evaluation of language resources, NLP datasets, evaluation, reproducibility
Contribution Types: Data resources
Languages Studied: English
Submission Number: 2273
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