Static Pruning in Dense Retrieval using Matrix Decomposition (2412.09983v1)

Abstract: In the era of dense retrieval, document indexing and retrieval is largely based on encoding models that transform text documents into embeddings. The efficiency of retrieval is directly proportional to the number of documents and the size of the embeddings. Recent studies have shown that it is possible to reduce embedding size without sacrificing - and in some cases improving - the retrieval effectiveness. However, the methods introduced by these studies are query-dependent, so they can't be applied offline and require additional computations during query processing, thus negatively impacting the retrieval efficiency. In this paper, we present a novel static pruning method for reducing the dimensionality of embeddings using Principal Components Analysis. This approach is query-independent and can be executed offline, leading to a significant boost in dense retrieval efficiency with a negligible impact on the system effectiveness. Our experiments show that our proposed method reduces the dimensionality of document representations by over 50% with up to a 5% reduction in NDCG@10, for different dense retrieval models.

• The paper introduces a static pruning method that reduces document embedding dimensions using PCA, enabling efficient dense retrieval without sacrificing effectiveness.
• By pruning over 50% of the dimensions and maintaining strong NDCG@10 scores, the approach significantly improves retrieval efficiency in models like ANCE, TAS-B, and Contriever.
• The method decouples query processing from dimensionality reduction by pre-computing embeddings offline, thereby reducing query latency and storage requirements in large-scale systems.

Static Pruning in Dense Retrieval using Matrix Decomposition

The paper "Static Pruning in Dense Retrieval using Matrix Decomposition" addresses a significant challenge in the domain of dense retrieval systems, specifically the trade-off between retrieval effectiveness and computational efficiency. This trade-off arises from the need to manage the high-dimensional embeddings generated by current dense retrieval models, which are critical for transforming text documents into representations suitable for effective indexing and retrieval. The paper introduces a novel static pruning approach to enhance efficiency by reducing dimensionality using Principal Components Analysis (PCA), an established mathematical technique to capture the most important features of high-dimensional data.

Key Contributions

1. Dimensionality Reduction: The paper presents a mechanism for static pruning that reduces the dimensionality of document embeddings in a query-independent manner. Unlike previous methods that often depend on query-specific computations, this approach allows dimensionality reduction to be executed offline without additional computational overhead during online query processing. The significant advantage here is improved retrieval efficiency without substantial losses in retrieval effectiveness.
2. PCA Application: Utilizing PCA, the paper introduces a process that can prune more than 50% of the embedding dimensions while maintaining retrieval quality. This technique leverages the principal components of the document embeddings, capturing the most variance and thus ensuring that the core information content remains intact despite dimensionality reduction.
3. Extensive Evaluations: The methodology is thoroughly evaluated using various dense retrieval models like ANCE, TAS-B, and Contriever, and across multiple query sets, including in-domain and out-of-domain scenarios. The results consistently show minimal impact on effectiveness metrics like NDCG@10 even at high pruning rates, demonstrating the robustness and applicability of the proposed method.
4. Query Independence: A noteworthy aspect of the paper is the emphasis on query-independent processing. By decoupling the dimensionality reduction process from query-specific information, the approach allows embeddings to be pre-processed, which can significantly decrease query latency and storage requirements for dense retrieval systems, making it well-suited for real-time applications that demand high throughput.

Implications and Future Directions

The implications of this research are manifold. Practically, the proposed static pruning technique provides a scalable solution to handle large-scale document corpora typical in modern information retrieval systems. The reduction in space and computational requirements without sacrificing retrieval performance aligns well with industry trends towards more efficient and cost-effective systems.

Theoretically, the application of PCA in this context also opens up avenues for further exploration of other matrix decomposition techniques, which might offer different trade-offs between retention of information and model compressibility. Future work could investigate adaptive mechanisms that determine the optimal number of dimensions to keep based on dynamic usage patterns or specific semantic needs of different applications.

Moreover, the potential of integrating such dimensionality reduction techniques with neural retrievers could enhance adaptability and performance further. Investigating methods beyond PCA, such as incorporating autoencoder-based dimensionality reduction, might provide additional leaps in efficiency while preserving semantic richness.

In conclusion, the proposed static pruning method using PCA showcased in this work provides a promising step forward in enhancing the performance of dense retrieval systems. Its ability to significantly reduce computational and storage overhead while maintaining robust retrieval quality offers a viable pathway for advancing both theoretical understanding and practical implementations in dense document retrieval setups.