DS$^2$-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning
Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. To address this, we introduce DS$^2$-Instruct, a zero-shot framework that generates domain-specific instruction datasets without human supervision. Our approach first generates task-informed keywords to ensure comprehensive domain coverage. It then creates diverse instructions by pairing these keywords with different cognitive levels from Bloom's Taxonomy. Finally, it uses self-consistency v...
| Authors | Ruiyao Xu, Noelle I. Samia, Han Liu |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 16 March 2026 |
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