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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 as a atomic GenIR model reproducing [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}
}
``` |