Automatically Generating Custom Context-Driven SFT Data for LLMs with Multi-Granularity

Constructing high-quality query-response pairs from custom corpora is crucial for supervised fine-tuning (SFT) large language models (LLMs) in many applications, like creating vertical-domain AI assistants or roleplaying agents. However, sourcing this data through human annotation is costly, and existing automated methods often fail to capture the diverse range of contextual granularity and tend to produce homogeneous data. To tackle these issues, we introduce a novel method named AugCon, capable of automatically generating context-driven SFT data across multiple levels of granularity with high diversity, quality and fidelity. AugCon begins by generating queries using the Context-Split-Tree (CST), an innovative approach for recursively deriving queries and splitting context to cover full granularity. Then, we train a scorer through contrastive learning to collaborate with CST to rank and refine queries. Finally, a synergistic integration of self-alignment and self-improving is introduced to obtain high-fidelity responses. The results highlight the significant advantages of AugCon in producing high diversity, quality, and fidelity SFT data against several state-of-the-art methods.