Simple, Efficient and Effective Approximation of SPLADE for Faster Information Retrieval

Khái niệm cốt lõi

A two-step approach to efficiently approximate SPLADE, a learned sparse retrieval model, while maintaining its effectiveness. The first step uses a pruned and reweighted version of SPLADE vectors for fast retrieval, and the second step rescores a sample of documents using the original SPLADE vectors.

Tóm tắt

The authors propose a two-step approach to efficiently approximate the SPLADE learned sparse retrieval model while maintaining its effectiveness.
In the first step, the authors prune both documents and queries to a fixed size based on the average token length in the dataset. They also introduce a term reweighting function that combines SPLADE term weights with BM25-style term frequency saturation. This allows for faster retrieval using dynamic pruning algorithms like WAND.
In the second step, the authors rescore the top k documents retrieved in the first step using the original, unpruned SPLADE vectors. This allows them to maintain the effectiveness of the full SPLADE model while significantly improving retrieval efficiency.
The authors extensively evaluate their two-step approach on 30 different in-domain and out-of-domain datasets. They show that their method can improve mean and tail response times over the original single-stage SPLADE processing by up to 30x and 40x respectively for in-domain datasets, and by 12x to 25x for mean response on out-of-domain datasets. This is achieved without incurring statistically significant effectiveness losses in 60% of the tested datasets.
The authors also compare their approach to prior work on efficient learned sparse retrieval, such as Guided Traversal and EfficientSPLADE, and show that their two-step method outperforms these baselines in both efficiency and effectiveness.

Thống kê

The authors report the following key statistics:

On MSMARCO-dev, their two-step approach achieves a 0.8x average latency compared to BM25, and a 0.4x p99 latency.
On the BEIR benchmark, their two-step approach achieves a 1.4x average latency compared to BM25, with improvements of up to 12x over the original SPLADE.
On the LoTTe benchmark, their two-step approach achieves a 1.3x average latency compared to BM25, with improvements of up to 25x over the original SPLADE.

Trích dẫn

"Our approximation allows us to propose new ranking models as efficient as GT, i.e., between 12× to 40× faster than SPLADE but more effective than GT, i.e., with statistically significant gains in 50% of the tested datasets and without statistically significant losses in 87% of the tested datasets."

Thông tin chi tiết chính được chắt lọc từ

Two-Step SPLADE: Simple, Efficient and Effective Approximation of SPLADE

by Carl... lúc arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13357.pdf

Yêu cầu sâu hơn

How could the two-step approach be further extended or generalized to work with other learned sparse retrieval models beyond SPLADE

The two-step approach can be extended to work with other learned sparse retrieval models by adapting the indexing and retrieval processes to suit the specific characteristics of each model. For instance, different models may have varying requirements for document and query pruning, term re-weighting, and saturation parameters. By customizing these aspects based on the underlying architecture and training of the specific model, the two-step approach can be generalized to accommodate a wide range of learned sparse retrieval models. Additionally, the integration of different models may necessitate adjustments in the indexing structures and retrieval algorithms to ensure compatibility and optimal performance across the pipeline.

What are the potential trade-offs or limitations of the term reweighting function used in the first step, and how could it be further optimized

The term reweighting function used in the first step of the two-step approach introduces a trade-off between efficiency and effectiveness. One potential limitation is the need to carefully tune the saturation parameter (𝑘1) to balance the impact of term frequencies on the scoring function. If 𝑘1 is set too high, it may lead to a loss of effectiveness as the approximation becomes too close to the original model, resulting in slower retrieval times. On the other hand, setting 𝑘1 too low may sacrifice accuracy for speed, affecting the quality of the retrieved results. To optimize the term reweighting function, a systematic exploration of different 𝑘1 values across diverse datasets can be conducted to identify the optimal setting that maximizes both efficiency and effectiveness. Additionally, machine learning techniques such as hyperparameter optimization or automated parameter search algorithms can be employed to fine-tune the reweighting function for improved performance.

How could the two-step approach be integrated into a larger multi-stage retrieval pipeline, and what additional benefits or challenges might arise in that context

The two-step approach can be seamlessly integrated into a larger multi-stage retrieval pipeline by serving as the initial retrieval stage, followed by subsequent stages for reranking or refinement. In this context, the benefits of the two-step approach, such as improved efficiency and effectiveness, can be leveraged to enhance the overall retrieval process. By incorporating the two-step retrieval as the first stage, the subsequent stages can focus on more complex ranking models or reranking strategies without compromising the speed of the initial retrieval. However, challenges may arise in coordinating the outputs of each stage, ensuring smooth transition between stages, and optimizing the overall pipeline for maximum retrieval performance. Additional benefits of integrating the two-step approach include the ability to handle a diverse range of datasets and query types effectively, while potential challenges may involve maintaining consistency in scoring mechanisms and managing the computational resources required for multi-stage retrieval.