Query-Adaptive Semantic Chunking for Retrieval-Augmented Generation: A Dynamic Strategy with Contextual Window Expansion
Researchers have introduced Query-Adaptive Semantic Chunking (QASC), a novel method for improving retrieval-augmented generation (RAG) systems. Unlike fixed or purely semantic chunking, QASC dynamically creates document segments by considering user queries. This approach uses cosine similarity to identify relevant sentences, expands context around these sentences to maintain coherence, and aggregates scores to ensure overall relevance. Evaluations show QASC significantly outperforms existing methods, achieving an 18-27% relative improvement in F1-score over fixed chunking and an 8-12% improvement over semantic and agentic chunking techniques. AI
IMPACT Improves RAG system performance by dynamically tailoring document retrieval to user queries, potentially enhancing the accuracy and relevance of AI-generated responses.