C-Mining：通过几何偏移无监督发现文化数据合成的种子
C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment
AI总结：
本文提出C-Mining框架，通过几何偏移发现文化种子，提升文化理解与推理能力，实验显示在CulturalBench-Hard上提升6.03分。
摘要：
实现大语言模型中的文化对齐越来越依赖合成数据生成。对于此类生成，最关键的初始步骤是种子筛选；然而，现有方法缺乏可量化的标准来选择这些种子。现有方法依赖于无法扩展的手动筛选或易受偏见影响的LLM提取，将文化特异性视为抽象概念而非可测量的信号。在本文中，我们通过提出C-Mining，一种无监督框架，将文化种子的发现从主观选择过程转变为可计算的数据挖掘公式。我们的方法利用了新颖的几何见解，利用预训练嵌入空间中文化概念的跨语言偏移作为可量化的发现信号。通过系统地识别这些区域，这些区域以明显的语言排斥性和几何隔离性为特征，同时积极过滤掉噪声，C-Mining自动从原始多语言语料库中提取高保真的文化点（CPs），而无需依赖人类或LLM监督，将准备成本降低了超过150倍。我们进一步利用挖掘的知识来引导多样化的指令微调数据集的合成。广泛的实验表明，这种以种子为中心的方法显著增强了文化理解和推理能力，在CulturalBench-Hard上实现了+6.03分的提升，并超越了最先进的基线，提供了一种可扩展、可量化的高质量文化数据合成解决方案。
英文摘要：
Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initial step is seed curation; however, current methods lack quantifiable standards for selecting these seeds. Existing approaches rely on unscalable manual curation or bias-prone LLM extraction, treating cultural specificity as an abstract concept rather than a measurable signal. In this paper, we address this "quantification gap" by proposing C-Mining, an unsupervised framework that transforms the discovery of cultural seeds from a subjective selection process into a computable data mining formulation. Our approach exploits a novel geometric insight, leveraging the cross-lingual misalignment of cultural concepts within pre-trained embedding spaces as a quantifiable discovery signal. By systematically identifying these regions characterized by pronounced linguistic exclusivity and geometric isolation, while actively filtering out noise, C-Mining automatically extracts high-fidelity Culture Points (CPs) from raw multilingual corpora without reliance on human or LLM supervision, reducing preparation costs by more than 150-fold. We further leverage the mined knowledge to steer the synthesis of diverse instruction-tuning datasets. Extensive experiments demonstrate that this seed-centric approach significantly enhances cultural understanding and reasoning capabilities, achieving a +6.03 point improvement on CulturalBench-Hard and surpassing state-of-the-art baselines, providing a scalable, quantifiable solution for high-quality cultural data synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
