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How Are Generative Retrieval and Multi-Vector Dense Retrieval Related To Each Other?
Overview
In recent years, there has been a surge in the adoption of pre-trained language models and neural-based retrieval models. One effective technique is Dense Retrieval (DR), which achieves great ranking performance. Multi-Vector Dense Retrieval (MVDR) techniques use several vectors to describe documents or queries.
Generative Retrieval (GR) is a paradigm shift in information retrieval, aiming to produce suitable document identifiers for a given query immediately. A recent study has established a connection between state-of-the-art multi-vector dense retrieval and generative retrieval.
Key Findings
The study discovered similarities between the two methods’ emphasis on semantic matching and training targets. It clarified how the loss function in GR can be rebuilt to resemble the unified MVDR framework by looking at the attention layer and prediction head of the algorithm. It also examined how GR differs from MVDR in terms of document encoding and alignment.
Both multi-vector dense retrieval and generative retrieval use the same framework to determine document relevance by adding the products of the query and document vectors and an alignment matrix. Generative retrieval makes use of this common foundation, using special techniques to calculate the alignment matrix and document token vectors.
Contributions
The team offered fresh insights into Generative Retrieval (GR) from a Multi-Vector Dense Retrieval (MVDR) perspective and presented a common paradigm for evaluating query-document relevance. They also explored how GR makes use of this framework by looking at special methods for document encoding and alignment matrix computation.
Several in-depth analytical experiments have highlighted the term-matching phenomenon and clarified the properties of different alignment directions in both GR and MVDR paradigms, contributing significantly to the empirical understanding of these retrieval methods.
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