@inproceedings{almeida-matos-2021-benchmarking,
    title = "Benchmarking a transformer-{FREE} model for ad-hoc retrieval",
    author = "Almeida, Tiago  and
      Matos, S{\'e}rgio",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.eacl-main.293/",
    doi = "10.18653/v1/2021.eacl-main.293",
    pages = "3343--3353",
    abstract = "Transformer-based {\textquotedblleft}behemoths{\textquotedblright} have grown in popularity, as well as structurally, shattering multiple NLP benchmarks along the way. However, their real-world usability remains a question. In this work, we empirically assess the feasibility of applying transformer-based models in real-world ad-hoc retrieval applications by comparison to a {\textquotedblleft}greener and more sustainable{\textquotedblright} alternative, comprising only 620 trainable parameters. We present an analysis of their efficacy and efficiency and show that considering limited computational resources, the lighter model running on the CPU achieves a 3 to 20 times speedup in training and 7 to 47 times in inference while maintaining a comparable retrieval performance. Code to reproduce the efficiency experiments is available on {\textquotedblleft}\url{https://github.com/bioinformatics-ua/EACL2021-reproducibility/}{\textquotedblleft}."
}