Link to the paper (published at SIGIR 2023)
This repository contains the code and dataset used to generate the ExaRanker model.
Summary:
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/ds: path to generate the dataset augmented with explanations
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/monoT5-bin-plus: finetunning and inference for the ExaRanker model
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/monoT5-bin: finetunning and inference for the T5 model (baseline model)
@inproceedings{10.1145/3539618.3592067,
author = {Ferraretto, Fernando and Laitz, Thiago and Lotufo, Roberto and Nogueira, Rodrigo},
title = {ExaRanker: Synthetic Explanations Improve Neural Rankers},
year = {2023},
isbn = {9781450394086},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3539618.3592067},
doi = {10.1145/3539618.3592067},
booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2409–2414},
numpages = {6},
keywords = {multi-stage ranking, few-shot models, explanations, large language models, synthetic datasets, generative models},
location = {<conf-loc>, <city>Taipei</city>, <country>Taiwan</country>, </conf-loc>},
series = {SIGIR '23}
}