用少量种子数据生成高质量任务数据，解决微调数据稀缺问题
AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning
- 通过多跳机制从少数种子数据逐步扩展，保持多样性与任务相关性
- 仅用10个种子数据即可微调Mistral-7B等模型，效果超过人工标注数据
- 适合数据稀缺场景，尤其适用于低资源任务的模型微调
为特定任务微调大语言模型需要多样且高质量的训练数据，但获取足够相关数据仍是重大挑战。现有数据合成方法要么依赖大量种子数据，要么难以平衡任务相关性与数据多样性。为此，我们提出属性引导的多跳数据扩展框架AIDE，通过多跳过程从极少数种子数据点出发，持续扩展数据，同时保证多样性和任务相关性。AIDE提取种子数据的主题与关键知识属性以指导生成步骤，并在每轮中使用生成数据作为新种子，共进行K次迭代。为防止深度增加导致生成数据偏离主题，引入残差连接机制。实验表明，AIDE仅用10个种子数据即可成功微调Mistral-7B、Llama-3.1-8B和Llama-3.2-3B模型，性能优于基于人工标注数据微调的模型。此外，AIDE在任务微调中相比Evol-Instruct等先进方法提升超30%。代码已开源：https://github.com/Code4Graph/AIDE。
原文摘要 · Abstract (English)
Fine-tuning large language models (LLMs) for specific tasks requires diverse, high-quality training data. However, obtaining sufficient relevant data remains a significant challenge. Existing data synthesis methods either depend on extensive seed datasets or struggle to balance task relevance and data diversity. To address these challenges, we propose Attribute-guided multI-hop Data Expansion (AIDE), a novel data synthesis framework that uses a multi-hop process to expand very few seed data points while ensuring data diversity and task relevance. AIDE extracts the main topic and key knowledge attributes from the seeds to guide the synthesis steps. The process repeats for K hops, using the generated data as seeds. To prevent irrelevant data generation as the hop depth increases, AIDE incorporates a residual connection mechanism. Our empirical results show that AIDE enables fine-tuning of Mistral-7B, Llama-3.1-8B and Llama-3.2-3B from 10 seeds, surpassing the models fine-tuned on human curated data. Furthermore, AIDE outperforms state-of-the-art data synthesis methods, such as Evol-Instruct, by over 30% in task-specific fine-tuning. Code is available at https://github.com/Code4Graph/AIDE.
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