Summary of Qwen2.5-32b: Leveraging Self-consistent Tool-integrated Reasoning For Bengali Mathematical Olympiad Problem Solving, by Saad Tahmid and Sourav Sarker
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving
by Saad Tahmid, Sourav Sarker
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper presents an innovative approach to solving mathematical problems in Bengali, developed for a competition. The method utilizes advanced deep learning models, including the Qwen 2.5 series, with improvements made through prompt engineering, model quantization, and Tool Integrated Reasoning (TIR). Various model architectures were explored, refined through translation techniques, Retrieval-Augmented Generation (RAG), and custom dataset curation. Manual hyperparameter tuning optimized parameters for adaptability and accuracy. The approach demonstrates the potential of advanced NLP techniques in solving Bengali mathematical problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves math problems in Bengali using special computer models. It’s an innovative way to solve hard calculations. The researchers used different models, like Qwen 2.5, and made them better by tweaking settings and adding new tools. They even tested it on custom-made datasets! This shows that advanced computer techniques can be super helpful for solving math problems in Bengali. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter » Nlp » Prompt » Quantization » Rag » Retrieval augmented generation » Translation
