@inproceedings{moon-etal-2024-anymal,
    title = "{A}ny{MAL}: An Efficient and Scalable Any-Modality Augmented Language Model",
    author = "Moon, Seungwhan  and
      Madotto, Andrea  and
      Lin, Zhaojiang  and
      Nagarajan, Tushar  and
      Smith, Matt  and
      Jain, Shashank  and
      Yeh, Chun-Fu  and
      Murugesan, Prakash  and
      Heidari, Peyman  and
      Liu, Yue  and
      Srinet, Kavya  and
      Damavandi, Babak  and
      Kumar, Anuj",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.emnlp-industry.98/",
    doi = "10.18653/v1/2024.emnlp-industry.98",
    pages = "1314--1332"
}