D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation
This addresses the problem of costly dataset creation for researchers and practitioners in domain-specific AI, though it is incremental as it builds on existing QA generation methods.
The paper tackles the scarcity of high-quality QA datasets for domain-specific LLMs by introducing D-SCoRE, a training-free pipeline that generates QA datasets from text, resulting in outperformance on benchmarks like SQuADShifts and Covid-QA.
The scarcity and high cost of high-quality question-answering (QA) datasets hinder supervised fine-tuning (SFT) for domain-specific large language models (LLMs). To address this, we introduce D-SCoRE, a training-free pipeline that utilizes LLMs and prompt engineering to produce diverse, high-quality QA datasets from arbitrary textual sources. D-SCoRE integrates $\textbf{D}$ocument-centric processing, $\textbf{S}$egmentation, $\textbf{Co}$T $\textbf{R}$easoning, and structured $\textbf{E}$xport to generate QA-COT datasets tailored for domain-aware SFT. Multi-dimensional control mechanisms, such as semantic role transformation, question type balancing, and counterfactual materials, enhance diversity and relevance, overcoming limitations of existing QA generation. LLMs fine-tuned on D-SCoRE-generated QA datasets, and human-annotated QA datasets (SQuAD, Covid-QA) are evaluated on SQuADShifts and Covid-QA test sets, with D-SCoRE outperforming across most domains. D-SCoRE generates six QA-CoT pairs with four-option counterfactual materials per 100-200-word text in 90 seconds using an 8B LLM on consumer-grade hardware. Its simplicity and scalability enable efficient QA generation and high-performance fine-tuning across domains.