Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations

Core Concepts

Seismic, a novel approximate nearest neighbor algorithm, enables efficient and effective retrieval over learned sparse embeddings by leveraging the concentration of importance property in these representations.

Abstract

The paper introduces Seismic, a novel approximate nearest neighbor (ANN) algorithm for efficient retrieval over learned sparse representations (LSR). LSR models like Splade and Efficient Splade encode text into sparse embeddings, where each dimension corresponds to a vocabulary term. While LSR offers advantages like interpretability and compatibility with inverted indexes, efficient retrieval remains challenging due to the statistical properties of the learned embeddings. Seismic makes three key contributions: It observes that LSR models tend to concentrate the majority of the L1 mass of a vector on a small subset of the dimensions. This "concentration of importance" property allows Seismic to approximate the inner product between a query and document by considering only the top entries. Seismic organizes the inverted index into geometrically-cohesive blocks, each with a summary vector. During query processing, Seismic quickly determines which blocks need to be evaluated by comparing the query's inner product with the block summaries. Seismic leverages the forward index to compute exact inner products for documents in selected blocks, providing a way to correct any approximation errors introduced by the block summaries. Experimental results on the Ms Marco and Natural Questions datasets show that Seismic outperforms state-of-the-art baselines, including the winning submissions to the 2023 BigANN Challenge, by a significant margin in terms of query latency while maintaining high retrieval accuracy. Seismic reaches sub-millisecond per-query latency, often one to two orders of magnitude faster than the baselines.

Stats

The top 10 entries of query vectors in the Ms Marco dataset account for 75% of the L1 mass on average. The top 50 entries of document vectors in the Ms Marco dataset account for 75% of the L1 mass on average. Keeping the top 10% of query entries and 20% of document entries preserves 85% of the full inner product on average. Keeping the top 12% of query entries and 25% of document entries preserves 90% of the full inner product on average.

Quotes

"Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design." "Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25."

Key Insights Distilled From

Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations

by Sebastian Br... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18812.pdf

Deeper Inquiries

How can the concentration of importance property be leveraged to improve retrieval efficiency in other types of sparse representations beyond learned embeddings

The concentration of importance property, as observed in learned sparse representations, can be leveraged to improve retrieval efficiency in other types of sparse representations by focusing on the most significant dimensions or features of the data. By identifying and prioritizing the coordinates that contribute the most to the overall representation, a similar approach to Seismic can be applied. This involves organizing the data into blocks or clusters based on the importance of specific dimensions, summarizing these blocks efficiently, and dynamically pruning them during query processing. For instance, in the context of image data represented as sparse vectors, the concentration of importance property can be used to identify key pixels or features that have the most impact on the overall image content. By organizing images into blocks based on these important pixels and summarizing them effectively, a retrieval algorithm can quickly identify relevant images based on a query's key features. This approach can significantly reduce the computational complexity of searching through high-dimensional sparse data while maintaining accuracy.

What are the potential drawbacks or limitations of the Seismic approach, and how could it be further improved or extended

While Seismic demonstrates significant improvements in retrieval efficiency for learned sparse representations, there are potential drawbacks and limitations to consider: Loss of Information: The summarization and quantization techniques used in Seismic may lead to a loss of information, potentially impacting the accuracy of retrieval results. Balancing the trade-off between efficiency and accuracy is crucial in optimizing the algorithm. Scalability: Seismic's performance may vary with the size of the dataset and the dimensionality of the sparse representations. Scaling the algorithm to handle larger datasets or higher-dimensional data may require further optimization and tuning. Hyperparameter Sensitivity: The performance of Seismic is influenced by hyperparameters such as 𝜆, 𝛽, and 𝛼. Finding the optimal values for these hyperparameters can be challenging and may require extensive experimentation. To further improve and extend Seismic, the following strategies can be considered: Adaptive Summarization: Implementing adaptive summarization techniques that adjust the level of summarization based on the characteristics of the data could enhance the algorithm's flexibility and performance. Dynamic Hyperparameter Tuning: Developing mechanisms to dynamically adjust hyperparameters during query processing based on the data distribution and query characteristics can optimize the algorithm's performance in real-time. Enhanced Pruning Strategies: Exploring more sophisticated pruning strategies that take into account the specific properties of different types of sparse representations can further enhance the efficiency of the algorithm.

Given the success of Seismic on learned sparse representations, how might it perform on other types of high-dimensional sparse data, such as in recommender systems or genomics

Given the success of Seismic on learned sparse representations, it has the potential to perform well on other types of high-dimensional sparse data, such as in recommender systems or genomics. Here's how Seismic might fare in these domains: Recommender Systems: In recommender systems, where user-item interactions are often represented as sparse vectors, Seismic could efficiently retrieve relevant items based on user preferences. By leveraging the concentration of importance property to identify key features in user-item interactions, Seismic could streamline the recommendation process and provide personalized recommendations with low latency. Genomics: In genomics, where genetic data is represented as high-dimensional sparse vectors, Seismic could be applied to retrieve relevant genetic information for research or clinical purposes. By organizing genetic data into blocks based on important genetic markers or sequences, Seismic could facilitate quick searches for specific genetic patterns or variations, aiding in genomic analysis and personalized medicine. Overall, Seismic's approach of organizing data into blocks, leveraging summaries for efficient retrieval, and dynamic pruning based on the concentration of importance could be beneficial in various domains dealing with high-dimensional sparse data. Further experimentation and optimization tailored to the specific characteristics of each domain would be necessary to maximize Seismic's performance.