🤖 AI Summary
This work addresses the limitations of traditional Retrieval-Augmented Generation (RAG) systems, which rely on static semantic chunking and struggle to balance retrieval precision and recall while lacking adaptability to query-specific context. The authors propose Query-Adaptive Semantic Chunking (QASC), a novel approach that integrates user query information directly into the chunking phase for the first time. QASC identifies seed sentences based on cosine similarity between sentences and the query, dynamically expands contextual windows, and leverages an embedding model combined with a chunk-level relevance aggregation mechanism to produce semantically coherent and query-relevant text chunks. Evaluated on 100 technical documents and 200 queries, QASC achieves an F1 score of 0.85—outperforming fixed-size chunking by 18–27% and surpassing existing semantic and agent-based chunking methods by 8–12%. High inter-annotator agreement in human evaluation further validates its effectiveness (Cohen’s κ = 0.82).
📝 Abstract
Retrieval-Augmented Generation (RAG) systems depend critically on document chunking quality for retrieving relevant context. Fixed chunking segments documents into uniform units irrespective of semantics or user intent, producing a precision-recall trade-off unresolvable by tuning chunk size alone. Semantic and agentic methods partially address these limitations but do not integrate user queries at the chunking stage. We present Query-Adaptive Semantic Chunking (QASC), which dynamically constructs chunks by integrating queries into segmentation through three mechanisms: cosine similarity scoring between sentence and query embeddings to identify seed sentences, contextual window expansion around seeds to preserve coherence, and chunk-level score aggregation to ensure holistic relevance. We evaluate QASC on 100 technical documents across 200 queries spanning four types, comparing against fixed chunking at five granularities, recursive splitting, semantic chunking, and agentic chunking. QASC achieves an F1-score of 0.85, a relative improvement of 18-27% over fixed chunking and 8-12% over semantic and agentic alternatives. Ablation studies confirm each component contributes meaningfully. Human evaluation by three annotators (Cohen kappa = 0.82) corroborates that QASC produces more relevant and coherent chunks than existing methods.