Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models

• URL: http://arxiv.org/abs/2406.12572v3
• Date: Tue, 15 Oct 2024 10:35:17 GMT
• Title: Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models
• Authors: Eldar Kurtic, Amir Moeini, Dan Alistarh,
• Abstract summary: We introduce Mathador-LM, a new benchmark for evaluating the mathematical reasoning on large language models (LLMs) Mathador-LM 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. We show that, across leading LLMs, we obtain stable average performance while generating benchmark instances emphdynamically, following a target difficulty level.
• Score: 34.814875040792344
• License:
• 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 \emph{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 3rd graders. This stands in stark contrast to their strong performance on popular mathematical reasoning benchmarks. The implementation of Mathador-LM benchmark is available at \href{https://github.com/IST-DASLab/Mathador-LM}{github.com/IST-DASLab/Mathador-LM}.

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