Is Semantic Chunking worth the computational cost?

Semantic chunking, a method used in Retrieval-Augmented Generation (RAG) systems to divide documents into semantically coherent segments, is being critically evaluated against the simpler fixed-size chunking approach. The study reveals that while semantic chunking aims to preserve context by grouping related sentences based on semantic coherence, it incurs significant computational overhead and does not consistently outperform fixed-size chunking in real-world scenarios. Fixed-size chunking, which divides documents into uniform segments, proves more efficient and often equally effective or superior, especially in typical document structures. The research utilized datasets from BEIR and RAGBench, employing F1@5 as an evaluation metric due to the unsuitability of traditional metrics like Recall@k. Results showed that semantic chunking displayed minimal advantages, particularly when documents were artificially stitched, and its benefits were inconsistent across different tasks. The study concludes that fixed-size chunking remains a robust strategy due to its simplicity, scalability, and adaptability, while high-quality embeddings play a crucial role in improving retrieval performance regardless of the chunking strategy employed.