Latent Query Alignment for Enhanced Domain-specific Retrieval-Augmented Generation

Large Language Models LLMs demonstrate impressive generalization capabilities in various natural language processing tasks but often encounter performance degradation, particularly in domain-specific applications due to hallucinations and semantic mismatches. We propose LaQuA, a novel retrieval framework leveraging latent query alignment to bridge the semantic gap between user queries and specialized domain documents. LaQuA integrates three core innovations: latent query generation using LLMs, contrastive alignment with similarity-constrained synthetic queries, and a semantic bridging inference mechanism employing proxy queries. Comprehensive experiments on public benchmarks and a custom domain-specific dataset show that LaQuA significantly improves retrieval quality compared to standard dense retrievers and pseudo-query approaches. Additionally, evaluation within a Retrieval-Augmented Generation RAG pipeline demonstrates consistent enhancements in factual accuracy and content relevance across multiple language models and domains. Our findings suggest that latent query-driven semantic alignment substantially mitigates hallucinations and improves LLM performance in knowledge-intensive, domain-specific tasks.