  Constructing high-quality query-response pairs from custom corpus is crucial
for supervised fine-tuning (SFT) large language models (LLMs) in many
applications, like creating domain-specific 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.
  Extensive experiments are conducted incorporating both human and automatic
evaluations, encompassing a test scenario and four widely-used benchmarks in
English and Chinese. The results highlight the significant advantages of AugCon
in producing high diversity, quality, and fidelity SFT data against several
state-of-the-art methods. All of our code, dataset, and fine-tuned model will
be available at: https://github.com/quanshr/AugCon.
