1. MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs
- Author
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Wang, Lei, Dong, Shan, Xu, Yuhui, Dong, Hanze, Wang, Yalu, Saha, Amrita, Lim, Ee-Peng, Xiong, Caiming, and Sahoo, Doyen
- Subjects
Computer Science - Computation and Language - Abstract
Recent large language models (LLMs) have demonstrated versatile capabilities in long-context scenarios. Although some recent benchmarks have been developed to evaluate the long-context capabilities of LLMs, there is a lack of benchmarks evaluating the mathematical reasoning abilities of LLMs over long contexts, which is crucial for LLMs' application in real-world scenarios. In this paper, we introduce MathHay, an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs. Unlike previous benchmarks like Needle in a Haystack, which focus primarily on information retrieval within long texts, MathHay demands models with both information-seeking and complex mathematical reasoning abilities. We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing LLMs. Even the best-performing model, Gemini-1.5-Pro-002, still struggles with mathematical reasoning over long contexts, achieving only 51.26% accuracy at 128K tokens. This highlights the significant room for improvement on the MathHay benchmark., Comment: Work-in-Progress
- Published
- 2024