تقرير
InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning
| العنوان: | InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning |
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| المؤلفون: | Zhang, Bo-Wen, Yan, Yan, Li, Lin, Liu, Guang |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, I.2.7 |
| الوصف: | Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT) methods have greatly enhanced language models' mathematical reasoning capabilities, facilitating their integration into instruction tuning datasets with LLMs. However, existing methods for large-scale dataset creation require substantial seed data and high computational costs for data synthesis, posing significant challenges for scalability. We introduce InfinityMATH, a scalable instruction tuning dataset for programmatic mathematical reasoning. The construction pipeline emphasizes decoupling numbers from mathematical problems to synthesize number-independent programs, enabling efficient and flexible scaling while minimizing dependency on specific numerical values. Fine-tuning experiments with open-source language and code models, such as Llama2 and CodeLlama, demonstrate the practical benefits of InfinityMATH. These fine-tuned models, showed significant relative improvements on both in-domain and out-of-domain benchmarks, ranging from 184.7% to 514.3% on average. Additionally, these models exhibited high robustness on the GSM8K+ and MATH+ benchmarks, which are enhanced version of test sets with simply the number variations. InfinityMATH ensures that models are more versatile and effective across a broader range of mathematical problems. The data is available at https://huggingface.co/datasets/flagopen/InfinityMATH. Comment: Accepted by CIKM 2024 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3627673.3679122 |
| URL الوصول: | http://arxiv.org/abs/2408.07089 |
| رقم الأكسشن: | edsarx.2408.07089 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3627673.3679122 |
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