SPLATE: An Efficient Sparse Late Interaction Retrieval Approach for Contextual Language Models

Core Concepts

SPLATE is a lightweight adaptation of the ColBERTv2 model that learns a sparse vocabulary-based representation, enabling efficient candidate generation for late interaction retrieval pipelines.

Abstract

The paper introduces SPLATE, a novel approach to efficiently implement the candidate generation step in late interaction retrieval pipelines based on contextual language models like ColBERTv2. The key insights are: SPLATE adapts the frozen token embeddings of ColBERTv2 by learning a lightweight "MLM adapter" module that maps the dense representations to a sparse vocabulary space. This allows SPLATE to leverage traditional sparse retrieval techniques for the candidate generation step. By bridging the gap between sparse and dense retrieval models, SPLATE can provide ColBERTv2 with a set of candidate documents to re-rank, while being particularly efficient in mono-CPU environments. Experiments show that SPLATE can achieve comparable effectiveness to the original ColBERTv2 pipeline, while greatly reducing the latency of the candidate generation step (e.g., from 186ms to around 10ms on MS MARCO). SPLATE also offers more interpretability, as the candidate generation operates directly in the vocabulary space, unlike previous optimized late interaction pipelines. Overall, SPLATE demonstrates how to efficiently integrate sparse and dense retrieval models, providing a practical solution to deploy ColBERTv2-like architectures in resource-constrained environments.

Stats

SPLATE with (kq, kd) = (5, 50) can retrieve over 90% of ColBERTv2's top-10 documents in its top-50 candidates. SPLATE (e2e) with (kq, kd) = (10, 100) and k = 50 achieves 40.0 MRR@10 on MS MARCO dev, on par with ColBERTv2 and PLAID ColBERTv2. The mean response time for SPLATE (R) with (kq, kd) = (5, 50) is only 2.9ms, compared to 186ms reported for the PLAID ColBERTv2 pipeline.

Quotes

"SPLATE is motivated by two core ideas: 1. PLAID [38] draws inspiration from traditional BoW retrieval to optimize the late interaction pipeline; 2. dense embeddings can seemingly be mapped to the vocabulary space [36]." "By adapting ColBERT's frozen dense representations with a SPLADE module, SPLATE aims to approximate late interaction scoring with an efficient sparse dot product."

Key Insights Distilled From

SPLATE: Sparse Late Interaction Retrieval

by Thib... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13950.pdf

Deeper Inquiries

How can the SPLATE approach be extended to jointly train the candidate generation and re-ranking modules, instead of relying on a two-stage pipeline

To extend the SPLATE approach to jointly train the candidate generation and re-ranking modules, a unified model architecture can be designed that incorporates both tasks in a single framework. This can involve modifying the existing SPLATE model to include additional components or layers that handle both candidate generation and re-ranking simultaneously. By integrating the training objectives of both tasks into a unified loss function, the model can learn to optimize both processes jointly, leading to improved efficiency and effectiveness in retrieval scenarios. This joint training approach can help the model better capture the interactions between candidate documents and queries, leading to more accurate and contextually relevant results.

What are the potential limitations of the SPLATE approach, and how could it be further improved to handle more challenging retrieval scenarios

One potential limitation of the SPLATE approach is its reliance on sparse representations, which may struggle in handling more complex retrieval scenarios with diverse and nuanced information needs. To address this limitation, SPLATE could be further improved by incorporating mechanisms for capturing semantic relationships and contextual information in the sparse representations. This could involve enhancing the model with additional contextual embeddings or leveraging pre-trained language models to enrich the sparse representations with more nuanced information. Furthermore, exploring ensemble methods or incorporating domain-specific knowledge could help enhance the model's performance in challenging retrieval scenarios where sparse representations alone may not suffice.

Given the interpretability of the SPLATE representations, how could they be leveraged for other tasks beyond retrieval, such as query understanding or explainable AI

The interpretability of SPLATE representations opens up opportunities for leveraging them in various tasks beyond retrieval, such as query understanding and explainable AI. These representations can be utilized to provide insights into how queries are matched with documents, enabling better understanding of the retrieval process. In query understanding, SPLATE representations can be used to analyze the semantic relevance between queries and documents, aiding in tasks like query expansion, intent detection, and relevance feedback. For explainable AI, the interpretable nature of SPLATE representations can help in providing transparent explanations for retrieval results, enabling users to understand why certain documents were retrieved and ranked higher than others. By leveraging SPLATE representations in these tasks, it is possible to enhance the transparency, interpretability, and overall performance of AI systems.