Semanlink - [2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

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sl:arxiv_author :
sl:arxiv_firstAuthor : Jinhyuk Lee
sl:arxiv_num : 2304.01982
sl:arxiv_published : 2023-04-04T17:37:06Z
sl:arxiv_summary : Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many information retrieval benchmarks. However, their non-linear scoring function cannot be scaled to millions of documents, necessitating a three-stage process for inference: retrieving initial candidates via token retrieval, accessing all token vectors, and scoring the initial candidate documents. The non-linear scoring function is applied over all token vectors of each candidate document, making the inference process complicated and slow. In this paper, we aim to simplify the multi-vector retrieval by rethinking the role of token retrieval. We present XTR, ConteXtualized Token Retriever, which introduces a simple, yet novel, objective function that encourages the model to retrieve the most important document tokens first. The improvement to token retrieval allows XTR to rank candidates only using the retrieved tokens rather than all tokens in the document, and enables a newly designed scoring stage that is two-to-three orders of magnitude cheaper than that of ColBERT. On the popular BEIR benchmark, XTR advances the state-of-the-art by 2.8 nDCG@10 without any distillation. Detailed analysis confirms our decision to revisit the token retrieval stage, as XTR demonstrates much better recall of the token retrieval stage compared to ColBERT.@en
sl:arxiv_title : Rethinking the Role of Token Retrieval in Multi-Vector Retrieval@en
sl:arxiv_updated : 2023-04-04T17:37:06Z
sl:bookmarkOf : https://arxiv.org/abs/2304.01982
sl:creationDate : 2023-04-05
sl:creationTime : 2023-04-05T08:33:18Z

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**Augment language model pre-training with a retriever module**, which
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> To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. **For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner**, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents

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[Google AI Blog Post](doc:2020/08/google_ai_blog_realm_integrat)

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