Summary of Multilingual Mathematical Reasoning: Advancing Open-source Llms in Hindi and English, by Avinash Anand et al.
Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi and English
by Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair, Manvendra Kumar Nema, Raj Jaiswal, Rajiv Ratn Shah
First submitted to arxiv on: 24 Dec 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 This research aims to enhance the mathematical reasoning skills of smaller, resource-efficient open-source Large Language Models (LLMs) in both Hindi and English. The study evaluates various models using zero-shot, few-shot chain-of-thought (CoT) methods and supervised fine-tuning. The approach incorporates curriculum learning, a Decomposition Strategy to simplify complex arithmetic operations, and a Structured Solution Design that divides solutions into phases. The results show notable performance enhancements, with WizardMath 7B exceeding Gemini’s accuracy on English datasets by +6% and matching Gemini’s performance on Hindi datasets. Adopting a bilingual approach combining English and Hindi samples achieves results comparable to individual language models, demonstrating the capability to learn mathematical reasoning in both languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to help computers get better at math problems. Right now, big language models are great at talking but struggle with math, especially if it’s not in English. The researchers want to make smaller language models that can do math problems well in Hindi and English too. They tested different ways of teaching the models, like using simpler math problems first and then moving on to harder ones. They also tried breaking down complex math operations into smaller steps. The results are promising, with one model doing better than others on certain math tests. It’s exciting because it means computers might be able to do math in more languages soon! |
Keywords
» Artificial intelligence » Curriculum learning » Few shot » Fine tuning » Gemini » Supervised » Zero shot
