用低成本生成高相关性数据，让专业大模型更懂行。
AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs
- 融合逻辑推理与自我检查机制，提升数据质量。
- 构建70.3万条数据，训练成本仅为深求的17%。
- 适合需要高效定制化数据的专业领域模型训练。
尽管大语言模型在通用领域表现优异，但在专业领域常表现不佳。现有数据合成方法虽能利用无标签数据捕捉领域特征，但或计算成本过高，或性能受限，且跨任务泛化能力不足。为此，我们提出AQuilt框架，从对应无标签数据中构建任意专业领域的指令微调数据，包含答案、问题、无标签数据、自检、逻辑和任务类型。通过引入逻辑推理与自检机制，增强模型的推理能力。可定制的任务指令确保生成数据适用于各类任务。最终，我们构建了包含70.3万条样本的数据集以训练高效的数据合成模型。实验表明，AQuilt性能接近DeepSeek-V3，但仅需其17%的生产成本。进一步分析显示，生成数据对下游任务具有更高相关性。源代码、模型与脚本已公开于https://github.com/Krueske/AQuilt。
原文摘要 · Abstract (English)
Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data synthesis methods and yield promising results by using unlabeled data to capture domain-specific features. However, these methods either incur high computational costs or suffer from performance limitations, while also demonstrating insufficient generalization across different tasks. To address these challenges, we propose AQuilt, a framework for constructing instruction-tuning data for any specialized domains from corresponding unlabeled data, including Answer, Question, Unlabeled data, Inspection, Logic, and Task type. By incorporating logic and inspection, we encourage reasoning processes and self-inspection to enhance model performance. Moreover, customizable task instructions enable high-quality data generation for any task. As a result, we construct a dataset of 703k examples to train a powerful data synthesis model. Experiments show that AQuilt is comparable to DeepSeek-V3 while utilizing just 17% of the production cost. Further analysis demonstrates that our generated data exhibits higher relevance to downstream tasks. Source code, models, and scripts are available at https://github.com/Krueske/AQuilt.
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