Despite the advancements in large language models (LLMs) for mathematical reasoning, solving competition-level math problems remains a significant challenge, especially for open-source LLMs without external tools. We introduce the MMIQC dataset, comprising a mixture of processed web data and synthetic question-response pairs, aimed at enhancing the mathematical reasoning capabilities of base language models. Models fine-tuned on MMIQC consistently surpass their counterparts in performance on the MATH benchmark across various model sizes. Notably, Qwen-72B-MMIQC achieves a 45.0% accuracy, exceeding the previous open-source state-of-the-art by 8.2% and outperforming the initial version GPT-4 released in 2023. Extensive evaluation results on Hungarian high school finals suggest that such improvement can generalize to unseen data. Our ablation study on MMIQC reveals that a large part of the improvement can be attributed to our novel augmentation method, Iterative Question Composing (IQC), which involves iteratively composing new questions from seed problems using an LLM and applying rejection sampling through another LLM.
LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Ubiquitous Knowledge Processing Lab (UKP Lab)（无处不在知识处理实验室）；Department of Computer Science（计算机科学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；Technische Universität Darmstadt（德累斯顿技术大学）
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
Alibaba Group（阿里巴巴集团）；University of Science and Technology of China（中国科学技术大学）；Institute of Dataspace, Hefei Comprehensive National Science Center（合肥综合性国家科学中心天枢研究院）
A Novel Mathematical Framework for Objective Characterization of Ideas
Department of Mechanical Engineering, Indian Institute of Science (IISc), Bangalore, India - 560012（机械工程系，印度科学研究院（IISc），班加罗尔，印度 - 560012）；Department of Design and Manufacturing (erstwhile CPDM), Indian Institute of Science (IISc), Bangalore, India - 560012（设计与制造系（原CPDM），印度科学研究院（IISc），班加罗尔，印度 - 560012）
本文提出一种自动化数学框架，将想法表示为高维向量并结合 UMAP、DBSCAN 和 PCA 量化多样性，以客观筛选 CAI 或人类生成的创意。
