专门训练模型生成高质量指令数据，效果远超传统方法。
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
- 用无提示掩码训练提升模型生成能力。
- 在少量数据下，TriviaQA准确率提升超4%，GSM8K超2%。
- 通过相关性与新颖性分析，理解合成数据质量。
大语言模型训练对多样化、高质量指令数据的需求日益增长。当前多数研究依赖标准微调模型进行提示工程生成合成数据，但这类模型优化目标是通用问答而非数据生成，存在根本缺陷。本文提出新范式NOMAD，探索专为数据生成设计的训练方式，发现两个关键因素：无提示掩码训练和合理选择训练集规模。NOMAD在有限数据条件下显著优于基线，在TriviaQA上提升超4%，在GSM8K上提升超2%。最后，通过‘相关性’与‘新颖性’视角解析合成数据，提供新理解。
原文摘要 · Abstract (English)
Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data. Recently, many works are exploring synthetic data generation using LLMs. However, they primarily focus on prompt engineering with standard supervised instruction-finetuned models, which contains a fundamental limitation: these models are optimized for general question-answering/problem-solving rather than data generation. We propose a paradigm shift named \textbf{NOMAD} by investigating how to specifically train models for data generation, demonstrating that this task differs significantly from training a classical LM. We identify two key factors: no-prompt-masked training and proper training set size selection. Our method, NOMAD, shows substantial improvements over baselines, achieving >4\% gains in TriviaQA and >2\% in GSM8K with limited training data. Finally, we offer new insights by interpreting synthetic data through the lenses of "relevance" and "novelty".
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