Summary of Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks, by Avinash Anand et al.
Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks
by Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla, Sanjana Sanjeev, Jatin Kumar, Adarsh Raj Shivam, Rajiv Ratn Shah
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: The rapid progress in natural language processing (NLP) systems and large language models (LLMs) has opened up opportunities in education and instructional methods. LLMs can provide tailored learning experiences and immediate feedback through accessible and cost-effective services. One application area is in solving mathematical problems, which requires deciphering complex problem statements and performing precise arithmetic calculations. However, evaluating the arithmetic capabilities of LLMs has received little attention. This paper introduces the “MathQuest” dataset, sourced from NCERT textbooks, to evaluate the performance of three prominent LLMs: LLaMA-2, WizardMath, and MAmmoTH. Fine-tuning experiments reveal that MAmmoTH-13B is the most proficient in solving mathematical problems, establishing it as a robust benchmark for addressing NCERT mathematics problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how big language models can help with learning math. Right now, these models are really good at answering questions and giving feedback, but they’re not great at doing math problems. The team created a special dataset of math problems to test the models’ skills. They used three different models to see which one was best at solving the problems. Surprisingly, one model called MAmmoTH-13B did the best job! This means that this model can be a useful tool for helping people learn and practice their math skills. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Llama » Natural language processing » Nlp
