An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

Core Concepts

Late-interaction models rely on token embeddings for relevance, requiring efficient pruning methods to reduce storage overhead.

Abstract

Authors analyze matching mechanisms of late-interaction models like ColBERT and COIL.
Proposed document pruning methods aim to reduce storage overhead without compromising efficiency.
Extensive experiments conducted on MS MARCO and BEIR datasets.
Comparison with sparse retrieval models like DeepImpact and uniCOIL.
Query Token Pruning proposed for ColBERT to reduce retrieval latency.

Stats

Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector.
The recently proposed late-interaction multi-vector models achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries.
We conduct extensive experiments on both in-domain and out-domain datasets.

Quotes

"ColBERT allows the sum-of-max operation to interact with any document tokens, but the document tokens which also appear in the query still obtain much higher attention scores."
"Tokens with different positions and IDF values contribute rather differently to the final relevance score."

Key Insights Distilled From

An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

by Qi Liu,Gang ... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13291.pdf

Deeper Inquiries

How do late-interaction models compare with traditional retrieval models in terms of efficiency

Late-interaction models, such as ColBERT and COIL, offer a balance between effectiveness and efficiency compared to traditional retrieval models. While traditional retrieval models rely on sparse representations and exact matches between queries and documents, late-interaction models use dense vector representations for both queries and documents. This allows them to perform soft matching in the vector space, leading to improved retrieval effectiveness. Additionally, late-interaction models can leverage approximate nearest neighbor search algorithms for efficient retrieval without compromising on effectiveness.

What are the implications of reducing storage overhead in late-interaction models

Reducing storage overhead in late-interaction models has significant implications for practical applications. By implementing document token pruning methods based on the analysis of matching mechanisms, it is possible to reduce the storage requirements of these models while maintaining their retrieval effectiveness. This reduction in storage overhead not only improves the efficiency of the search system but also minimizes resource consumption, making it more cost-effective to deploy these advanced neural retrieval models at scale.

How can the findings from this study be applied to improve other neural retrieval models

The findings from this study can be applied to improve other neural retrieval models by incorporating similar token pruning strategies. By understanding how different tokens contribute to the final relevance score in late-interaction models like ColBERT and COIL, researchers can develop heuristic pruning methods that prioritize important tokens while discarding less relevant ones. These pruning techniques can be adapted and implemented in other neural retrieval architectures to enhance efficiency without sacrificing performance quality.