---
language:
- en
base_model:
- google-t5/t5-large
pipeline_tag: text-classification
tags:
- gen-ir
- information-retrieval
- ir
---
This repository contains one of the models analyzed in our paper [Reverse-Engineering the Retrieval Process in GenIR Models](https://dl.acm.org/doi/abs/10.1145/3726302.3730076).

### Training
The model is based on T5-large and was trained on the TriviaQA dataset (with only generated questions as input) as a atomic GenIR model using the setup of [DSI](https://arxiv.org/abs/2202.06991). 

### Model Overview
| Model        | Huggingface URL                                                         |
| ------------ | ----------------------------------------------------------------------- |
| NQ10k        | [DSI-large-NQ10k](https://huggingface.co/AnReu/DSI-large-NQ10k)         |
| NQ100k       | [DSI-large-NQ100k](https://huggingface.co/AnReu/DSI-large-NQ100k)       |
| NQ320k       | [DSI-large-NQ320k](https://huggingface.co/AnReu/DSI-large-NQ320k)       |
| Trivia-QA    | [DSI-large-TriviaQA](https://huggingface.co/AnReu/DSI-large-TriviaQA)   |
| Trivia-QA QG | [DSI-large-TriviaQA QG](https://huggingface.co/AnReu/DSI-large-TriviaQA-QG) |
### Citation
```
@inproceedings{Reusch2025Reverse,
  author = {Reusch, Anja and Belinkov, Yonatan},
  title = {Reverse-Engineering the Retrieval Process in GenIR Models},
  year = {2025},
  isbn = {9798400715921},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3726302.3730076},
  doi = {10.1145/3726302.3730076},
  booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages = {668–677},
  numpages = {10},
  location = {Padua, Italy},
  series = {SIGIR '25}
}
```