@inproceedings{degenaro-etal-2025-fortify,
    title = "{FORTIFY}: Generative Model Fine-tuning with {ORPO} for {R}e{T}rieval Expansion of {I}n{F}ormal {N}ois{Y} Text",
    author = "DeGenaro, Dan  and
      Yang, Eugene  and
      Etter, David  and
      Carpenter, Cameron  and
      Sanders, Kate  and
      Martin, Alexander  and
      Murray, Kenton  and
      Kriz, Reno",
    editor = "Kriz, Reno  and
      Murray, Kenton",
    booktitle = "Proceedings of the 1st Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2025)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.magmar-1.13/",
    pages = "100--115",
    ISBN = "979-8-89176-280-0",
    abstract = "Despite recent advancements in neural retrieval, representing text fragments or phrases with proper contextualized embeddings is still challenging. Particularly in video retrieval, where documents are text extracted through OCR from the frames or ASR from audio tracks, the textual content is rarely complete sentences but only a bag of phrases. In this work, we propose FORTIFY, a generative model fine-tuning approach for noisy document rewriting and summarization, to improve the downstream retrieval effectiveness. By experimenting on MultiVENT 2.0, an informational video retrieval benchmark, we show Llama fine-tuned with FORTIFY provides an effective document expansion, leading to a 30{\%} improvement over prompting an out-of-box Llama model on nDCG@10. Zero-shot transferring the model tailored for MultiVENT 2.0 to two out-of-distribution datasets still demonstrates competitive retrieval effectiveness to other document preprocessing alternatives."
}