Efficient Block-Max Pruning for Faster Learned Sparse Retrieval

Alapfogalmak

Block-Max Pruning (BMP) is an innovative dynamic pruning strategy that efficiently processes indexes generated by learned sparse retrieval models, outperforming existing methods in both safe and approximate retrieval scenarios.

Kivonat

The paper introduces Block-Max Pruning (BMP), a novel query processing strategy optimized for indexes generated by learned sparse retrieval models. BMP employs a block filtering mechanism to prioritize clusters of documents based on their potential relevance, using an optimized computation of range-based upper bounds. It evaluates promising subsets of documents through a hybrid between inverted and forward index structures.

The key highlights and insights are:

1. Learned sparse retrieval models, such as SPLADE, ESPLADE, and uniCOIL, exhibit structural variations in query and document statistics compared to traditional retrieval models, leading to performance discrepancies with existing query optimization techniques.

2. BMP substantially outperforms existing dynamic pruning strategies like MaxScore, BlockMaxWand, Anytime, and Clipping, offering 2.9x to 7.5x faster query processing times for safe retrieval on the SPLADE model.

3. For approximate retrieval, BMP achieves the best trade-off between efficiency and effectiveness, with sub-millisecond average response times and negligible loss in precision compared to exhaustive search.

4. BMP's efficiency is further improved by up to 2.5x when using a raw block-max index, without compression, demonstrating the effectiveness of the block-based pruning approach.

5. The paper also explores query term pruning as an additional approximation mechanism within the BMP framework, achieving sub-millisecond retrieval times with only a slight decrease in precision.

Overall, the proposed BMP strategy represents a significant advancement in optimizing learned sparse retrieval, addressing the efficiency challenges and enabling faster and more effective query processing.

Statisztikák

SPLADE is 11.5x to 58.5x slower than BMP for safe retrieval of top-10 to top-1000 results.
ESPLADE is 3.6x to 11.9x slower than the fastest BMP configuration for safe retrieval.
uniCOIL is 3.3x to 13.9x slower than the fastest BMP configuration for safe retrieval.

Idézetek

"BMP substantially outperforms existing dynamic pruning strategies, offering unparalleled efficiency in safe retrieval contexts and improved trade-offs between precision and efficiency in approximate retrieval tasks."
"For approximate retrieval, BMP achieves the highest effectiveness with the smallest mean response time across all models."
"Notably, with our proposed approach, both ESPLADE and uniCOIL achieve sub-millisecond average response times, with a negligible loss of no more than 1% in RR@10 compared to the exhaustive scenario."

Főbb Kivonatok

Faster Learned Sparse Retrieval with Block-Max Pruning

by Antonio Mall... : arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01117.pdf

Mélyebb kérdések

How can the block-based pruning approach in BMP be extended or adapted to other types of retrieval models beyond learned sparse representations?

The block-based pruning approach in BMP can be extended to other retrieval models by considering the underlying principles of the technique. One way to adapt this approach is to modify the block size and filtering mechanism based on the characteristics of the specific retrieval model. For instance, in models that rely on dense vector representations, the block size and aggregation strategy may need to be adjusted to accommodate the dense vectors efficiently. Additionally, the concept of block filtering can be applied to various indexing structures, not just inverted indexes, by redefining how document ranges are divided and processed. By understanding the core idea of prioritizing clusters of documents based on relevance, this approach can be tailored to different retrieval models to enhance efficiency and speed of query processing.

What are the potential trade-offs or limitations of the BMP strategy, and how could it be further improved or combined with other optimization techniques?

One potential trade-off of the BMP strategy is the balance between efficiency and effectiveness. While BMP excels in providing fast query processing, there might be a slight compromise in the precision of results, especially in approximate retrieval scenarios. To address this, fine-tuning the termination conditions and early stopping criteria can help optimize the trade-off between speed and accuracy. Additionally, BMP could be enhanced by integrating machine learning techniques to dynamically adjust parameters based on query characteristics and dataset properties. Combining BMP with techniques like query term pruning or query expansion could further improve its performance by refining the relevance estimation process and enhancing the quality of retrieved results.

Given the focus on efficiency, how might the BMP approach impact other aspects of the information retrieval system, such as indexing, storage, or energy consumption, and what implications could this have for real-world deployments?

The BMP approach's emphasis on efficiency can have significant implications for various aspects of the information retrieval system. In terms of indexing, BMP may require additional storage for maintaining block-based structures, especially for smaller block sizes. However, this could be offset by the potential reduction in index size due to optimized processing and filtering mechanisms. Moreover, the efficient query processing enabled by BMP could lead to lower energy consumption, making it a more sustainable option for large-scale retrieval systems. Real-world deployments of BMP could benefit from reduced latency, improved scalability, and lower operational costs, making it an attractive choice for organizations seeking to enhance the performance of their information retrieval systems.