Abstract

The development of reliable Large Language Models (LLMs) for specialized domains is fundamentally bottlenecked by the scarcity of high-quality training and evaluation data. In fields requiring deep expertise, generating such data is often prohibitively expensive due to the need for manual expert annotation. Moreover, strict privacy constraints frequently restrict access to existing real-world datasets. This lack of accessible data severely limits both the adaptation of models and their rigorous evaluation on specialized tasks.

Current state-of-the-art approaches often rely on quick fixes, such as naively prompting general-purpose LLMs to generate synthetic training data. While synthetic data generation is a promising direction overall, such ungrounded generation is highly prone to errors, hallucinations, and bias propagation. The underlying models are typically not domain experts, and crucially, robust quality assurance mechanisms are still missing to ensure the synthetic data truly aligns with the requirements of specialized fields. 

To address these open problems, this project proposes the development of a comprehensive, automated pipeline specifically designed for highly specialized, low-data regimes. The core objective is to create a framework for synthetic data generation that emphasizes grounding the output in domain data. By grounding the generation process directly in domain data (e.g., curated documents, terminology, and domain constraints), the pipeline will improve contextual accuracy and reduce factual drift. This generation process will be coupled with intelligent validation workflows to automatically assess and filter the generated data, ensuring it meets domain-specific standards before being used for model adaptation, training, or evaluation.

Research Questions

RQ1: How can synthetic data be generated such that it matches domain constraints and task requirements?

RQ2: How can domain-specific knowledge be effectively integrated into LLM-driven synthetic data generation to ensure contextual accuracy and mitigate errors?

RQ3: What automated evaluation frameworks can be developed to reliably validate the quality, and domain-alignment of synthetic data without requiring constant human expert intervention?

RQ4: To what extent does training on this validated, grounded synthetic data improve the downstream performance of models in specialized domains compared to traditional annotation in low-data (few-shot) settings?