Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

2026-05-28 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The DOMINO framework introduces a novel inductive paradigm for domain-specific data synthesis for Large Language Models, addressing the challenge of acquiring high-quality data when domain characteristics are difficult to articulate in natural language. Unlike existing deductive approaches that rely on explicit domain descriptions and prompt engineering, DOMINO learns a minimal sufficient domain representation directly from a set of reference examples. It integrates prompt tuning with a contrastive disentanglement objective to separate domain-level patterns from sample-specific noise, thereby mitigating overfitting and preserving core domain characteristics. Theoretically, DOMINO expands the support of the synthetic data distribution, ensuring greater diversity. Empirically, fine-tuning on data synthesized by DOMINO improved Pass@1 accuracy by up to 4.63% over strong, instruction-tuned backbones on challenging coding benchmarks, demonstrating its effectiveness and robustness for practical and scalable domain adaptation.

Key takeaway

For Machine Learning Engineers adapting Large Language Models to niche domains with implicit characteristics, DOMINO provides a robust solution for data synthesis. You can generate high-quality, domain-aligned synthetic data by providing only reference examples, eliminating the need for complex prompt engineering or explicit domain descriptions. This approach can significantly improve LLM performance, as demonstrated by a 4.63% Pass@1 accuracy increase on coding benchmarks, making domain adaptation more practical and scalable.

Key insights

DOMINO inductively synthesizes domain-specific data for LLMs by learning minimal representations from reference examples, bypassing explicit domain descriptions.

Principles

• Inductive data synthesis from examples.
• Disentangle domain patterns from noise.
• Expand synthetic data distribution support.

Method

DOMINO learns a minimal sufficient domain representation from reference samples using prompt tuning and a contrastive disentanglement objective, then guides LLM generation of domain-aligned synthetic data.

In practice

• Adapt LLMs to implicit coding domains.
• Generate data without manual prompt design.
• Improve LLM accuracy on specialized tasks.

Topics

• Large Language Models
• Data Synthesis
• Domain Adaptation
• Representation Learning
• Prompt Tuning
• Coding Benchmarks

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data — Structured pedagogical formats and diverse outputs are key to high-quality synthetic pretraining data, not larger generator models.
• Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding — Domino accelerates LLM inference by decoupling causal modeling from autoregressive drafting, using parallel generation refined by causal information.
• Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines — Domain-specific synthetic data and reinforcement learning significantly enhance LLM performance on specialized clinical knowledge.
• A family of large language models for materials research with insights into model adaptability in continued pretraining — Domain-adapted LLMs significantly enhance materials research, but overtraining can hinder further specialization.
• DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation — DALM uses domain algebra and structured denoising to prevent cross-domain contamination and hallucination in language models.
• Recovering Diversity Without Losing Alignment: A DPO Recipe for Post-Trained LLMs — Post-training LLMs can regain output diversity without losing alignment by carefully constructing DPO preference data.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.