Enhancing efficiency in large-scale search: from traditional to neural retrieval systems

Date

2026-2

Author

Yafay, Erman

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Efficient retrieval of relevant documents from massive collections remains an essen- tial challenge in Information Retrieval (IR). Modern search engines face immense computational demands, requiring novel approaches that reduce resource usage with- out sacrificing retrieval effectiveness. This thesis makes significant contributions to improving search efficiency through three distinct methods. First, we introduce in- novative document reordering techniques specifically optimized for dynamic pruning algorithms. Our proposed methods achieve up to 1.33x speed-up in query processing, accompanied by negligible increases in index size and minimal impact on retrieval quality. Second, we present novel sparse centroid retrieval strategies tailored to the ColBERT neural retrieval model. These techniques accelerate ColBERT-based re- trieval by up to 4.6x while maintaining high effectiveness and minimal additional indexing overhead. Lastly, we propose novel static pruning methods for ColBERT document embeddings that eliminate approximately one-third of the tokens from in- dexed documents without any loss in retrieval effectiveness. Critically, our pruning methods require no separate training stages, ensuring ease of integration into existing retrieval systems. Collectively, these contributions offer substantial advancements in retrieval efficiency, making large-scale IR systems faster, more scalable, and econom- ically sustainable.

Subject Keywords

Efficient document retrieval, Dynamic pruning, Document re-ordering, Dense retrieval, Sparse retrieval

URI

https://hdl.handle.net/11511/118794

Collections

Graduate School of Natural and Applied Sciences, Thesis