Domain-specific Question Answering with Hybrid Search
This addresses the complexities of domain-specific question answering in enterprise settings, though it appears incremental.
The paper tackles domain-specific question answering by proposing a hybrid approach that combines fine-tuned dense retrieval with keyword-based sparse search methods, resulting in improved accuracy over single-retriever systems.
Domain specific question answering is an evolving field that requires specialized solutions to address unique challenges. In this paper, we show that a hybrid approach combining a fine-tuned dense retriever with keyword based sparse search methods significantly enhances performance. Our system leverages a linear combination of relevance signals, including cosine similarity from dense retrieval, BM25 scores, and URL host matching, each with tunable boost parameters. Experimental results indicate that this hybrid method outperforms our single-retriever system, achieving improved accuracy while maintaining robust contextual grounding. These findings suggest that integrating multiple retrieval methodologies with weighted scoring effectively addresses the complexities of domain specific question answering in enterprise settings.