@inproceedings{lu-etal-2024-hyrr,
    title = "{HYRR}: Hybrid Infused Reranking for Passage Retrieval",
    author = "Lu, Jing  and
      Hall, Keith  and
      Ma, Ji  and
      Ni, Jianmo",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.748",
    pages = "8528--8534",
    abstract = "Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. To obtain an effective reranking model, many prior works have focused on improving the model architectures, such as leveraging powerful pretrained large language models (LLM) and designing better objective functions. However, less attention has been paid to the issue of collecting high-quality training data. In this paper, we propose HYRR, a framework for training robust reranking models. Specifically, we propose a simple but effective approach to select training data using hybrid retrievers. Our experiments show that the rerankers trained with HYRR are robust to different first-stage retrievers. Moreover, evaluations using MS MARCO and BEIR data sets demonstrate our proposed framework effectively generalizes to both supervised and zero-shot retrieval settings.",
}

Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. To obtain an effective reranking model, many prior works have focused on improving the model architectures, such as leveraging powerful pretrained large language models (LLM) and designing better objective functions. However, less attention has been paid to the issue of collecting high-quality training data. In this paper, we propose HYRR, a framework for training robust reranking models. Specifically, we propose a simple but effective approach to select training data using hybrid retrievers. Our experiments show that the rerankers trained with HYRR are robust to different first-stage retrievers. Moreover, evaluations using MS MARCO and BEIR data sets demonstrate our proposed framework effectively generalizes to both supervised and zero-shot retrieval settings.
Lu, Jing Hall, Keith Ma, Ji Ni, Jianmo
HYRR: Hybrid Infused Reranking for Passage Retrieval