AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning

TL;DR

AIDE is a novel data synthesis framework that uses attribute-guided multi-hop expansion to generate diverse, relevant training data from very few seeds, significantly improving task-specific fine-tuning of large language models.

Contribution

This paper introduces AIDE, a multi-hop data expansion method guided by attributes, which effectively synthesizes relevant data from minimal seeds for fine-tuning large language models.

Findings

AIDE enables effective fine-tuning from just 10 seed data points.

AIDE outperforms existing data synthesis methods like Evol-Instruct by over 30%.

AIDE improves fine-tuning results across multiple large language models.

Abstract

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.