readme: ---
  language: en
  tags:
  - question-answering
  ---
  
  # ReAtt
  ReAtt is a retrieval-augmented model for knowledge-intensive tasks proposed in [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://arxiv.org/pdf/2212.02027.pdf). The original Github repository is [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt).
  
  ## Description
  `neulab/reatt-large-nq-fid` (based on T5 architecture) is initialized with `neulab/reatt-large-nq` and adapted on FiQA dataset with end-to-end retrieval-augmented training.
  
  ## Usage
  Please refer to [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt) for instructions to use this model.
  
  ## Reference
  
  ```bibtex
  @inproceedings{jiang-etal-2022-reatt,
      title = {Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer},
      author = {Zhengbao Jiang and Luyu Gao and Jun Araki and Haibo Ding and Zhiruo Wang and Jamie Callan and Graham Neubig},
      booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      address = {Abu Dhabi, UAE},
      month = {December},
      year = {2022}
  }
  ```
  