Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks
<|reference_start|>Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks: The rapid progress in the field of natural language processing (NLP) systems and the expansion of large language models (LLMs) have opened up numerous opportunities in the field of education and instructional methods. These advancements offer the potential for tailored learning experiences and immediate feedback, all delivered through accessible and cost-effective services. One notable application area for this technological advancement is in the realm of solving mathematical problems. Mathematical problem-solving not only requires the ability to decipher complex problem statements but also the skill to perform precise arithmetic calculations at each step of the problem-solving process. However, the evaluation of the arithmetic capabilities of large language models remains an area that has received relatively little attention. In response, we introduce an extensive mathematics dataset called "MathQuest" sourced from the 11th and 12th standard Mathematics NCERT textbooks. This dataset encompasses mathematical challenges of varying complexity and covers a wide range of mathematical concepts. Utilizing this dataset, we conduct fine-tuning experiments with three prominent LLMs: LLaMA-2, WizardMath, and MAmmoTH. These fine-tuned models serve as benchmarks for evaluating their performance on our dataset. Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems. Consequently, MAmmoTH-13B establishes itself as a robust and dependable benchmark for addressing NCERT mathematics problems.<|reference_end|>
@article{anand2024mathify:,
    title={Mathify: Evaluating Large Language Models on Mathematical Problem
  Solving Tasks},
    author={Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla, Sanjana
  Sanjeev, Jatin Kumar, Adarsh Raj Shivam, Rajiv Ratn Shah},
    journal={NeurIPS 2023 Workshop on Generative AI for Education (GAIED)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13099},
    primaryClass={cs.CL cs.AI}
}