ExpertGenQA: Open-ended QA generation in Specialized Domains
This addresses the problem of limited QA generation for technical domains, offering an incremental improvement over existing methods.
The paper tackled the challenge of generating high-quality question-answer pairs for specialized technical domains by introducing ExpertGenQA, a protocol that combines few-shot learning with structured categorization, resulting in twice the efficiency of baselines and a 13.02% improvement in retrieval model accuracy.
Generating high-quality question-answer pairs for specialized technical domains remains challenging, with existing approaches facing a tradeoff between leveraging expert examples and achieving topical diversity. We present ExpertGenQA, a protocol that combines few-shot learning with structured topic and style categorization to generate comprehensive domain-specific QA pairs. Using U.S. Federal Railroad Administration documents as a test bed, we demonstrate that ExpertGenQA achieves twice the efficiency of baseline few-shot approaches while maintaining $94.4\%$ topic coverage. Through systematic evaluation, we show that current LLM-based judges and reward models exhibit strong bias toward superficial writing styles rather than content quality. Our analysis using Bloom's Taxonomy reveals that ExpertGenQA better preserves the cognitive complexity distribution of expert-written questions compared to template-based approaches. When used to train retrieval models, our generated queries improve top-1 accuracy by $13.02\%$ over baseline performance, demonstrating their effectiveness for downstream applications in technical domains.