IncDSI: Incrementally Updatable Document Retrieval

• URL: http://arxiv.org/abs/2307.10323v2
• Date: Mon, 19 Aug 2024 07:02:19 GMT
• Title: IncDSI: Incrementally Updatable Document Retrieval
• Authors: Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q. Weinberger,
• Abstract summary: IncDSI is a method to add documents in real time without retraining the model on the entire dataset. We formulate the addition of documents as a constrained optimization problem that makes minimal changes to the network parameters. Our approach is competitive with re-training the model on the whole dataset.
• Score: 35.5697863674097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents. These models have achieved state-of-the-art performances for document retrieval across many benchmarks. These kinds of models have a significant limitation: it is not easy to add new documents after a model is trained. We propose IncDSI, a method to add documents in real time (about 20-50ms per document), without retraining the model on the entire dataset (or even parts thereof). Instead we formulate the addition of documents as a constrained optimization problem that makes minimal changes to the network parameters. Although orders of magnitude faster, our approach is competitive with re-training the model on the whole dataset and enables the development of document retrieval systems that can be updated with new information in real-time. Our code for IncDSI is available at https://github.com/varshakishore/IncDSI.

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