Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation

التفاصيل البيبلوغرافية
العنوان: Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation
المؤلفون: Kang, Xiaoqiang, Wang, Zimu, Jin, Xiaobo, Wang, Wei, Huang, Kaizhu, Wang, Qiufeng
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language
الوصف: Solving tabular math word problems (TMWPs) has become a critical role in evaluating the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP samples are commonly required for LLM fine-tuning. Since the collection of high-quality TMWP datasets is costly and time-consuming, recent research has concentrated on automatic TMWP generation. However, current generated samples usually suffer from issues of either correctness or diversity. In this paper, we propose a Template-driven LLM-paraphrased (TeLL) framework for generating high-quality TMWP samples with diverse backgrounds and accurate tables, questions, answers, and solutions. To this end, we first extract templates from existing real samples to generate initial problems, ensuring correctness. Then, we adopt an LLM to extend templates and paraphrase problems, obtaining diverse TMWP samples. Furthermore, we find the reasoning annotation is important for solving TMWPs. Therefore, we propose to enrich each solution with illustrative reasoning steps. Through the proposed framework, we construct a high-quality dataset TabMWP-TeLL by adhering to the question types in the TabMWP dataset, and we conduct extensive experiments on a variety of LLMs to demonstrate the effectiveness of TabMWP-TeLL in improving TMWP solving performance. The code and data of this paper are available at: https://github.com/Jason8Kang/TELL.
Comment: Accepted at AAAI 2025, extended version with appendix
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2412.15594
رقم الانضمام: edsarx.2412.15594
قاعدة البيانات: arXiv