Transformer Memory as a Differentiable Search Index
Abstract
In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process. We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI significantly outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup.
Cite
Text
Tay et al. "Transformer Memory as a Differentiable Search Index." Neural Information Processing Systems, 2022.Markdown
[Tay et al. "Transformer Memory as a Differentiable Search Index." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/tay2022neurips-transformer/)BibTeX
@inproceedings{tay2022neurips-transformer,
title = {{Transformer Memory as a Differentiable Search Index}},
author = {Tay, Yi and Tran, Vinh and Dehghani, Mostafa and Ni, Jianmo and Bahri, Dara and Mehta, Harsh and Qin, Zhen and Hui, Kai and Zhao, Zhe and Gupta, Jai and Schuster, Tal and Cohen, William W. and Metzler, Donald},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/tay2022neurips-transformer/}
}