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: this https URL

通过创造高质量的查询 - 回应对，我们引入了一种名为 AugCon 的新方法，能够自动生成多个层次上的具有高多样性、质量和准确性的上下文驱动的 SFT 数据。实验证明 AugCon 在生成高多样性、质量和准确性的 SFT 数据方面具有明显优势。