MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs
<|reference_start|>MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs: 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.<|reference_end|>
@article{wang2024mathhay:,
    title={MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning
  in LLMs},
    author={Lei Wang, Shan Dong, Yuhui Xu, Hanze Dong, Yalu Wang, Amrita Saha,
  Ee-Peng Lim, Caiming Xiong, Doyen Sahoo},
    journal={arXiv preprint arXiv:2410.04698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04698},
    primaryClass={cs.CL}
}