@inproceedings{inan-etal-2025-signalignlm,
    title = "{S}ign{A}lign{LM}: Integrating Multimodal Sign Language Processing into Large Language Models",
    author = "Inan, Mert  and
      Sicilia, Anthony  and
      Alikhani, Malihe",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.190/",
    doi = "10.18653/v1/2025.findings-acl.190",
    pages = "3691--3706",
    ISBN = "979-8-89176-256-5",
    abstract = "Deaf and Hard-of-Hearing (DHH) users increasingly utilize Large Language Models (LLMs), yet face significant challenges due to these models' limited understanding of sign language grammar, multimodal sign inputs, and Deaf cultural contexts. Further, current approaches that try to address these limitations, frequently reduce sign language processing (SLP) to traditional translation tasks, neglecting the multimodal and linguistic complexity inherent in signed languages. In this paper, we present an empirical investigation informed by learning theory into natively integrating sign language support within LLMs, directly addressing the documented needs of DHH users. We introduce the first text-based and multimodal LLMs capable of sign language processing called SignAlignLM, and propose new prompting and fine-tuning strategies incorporating sign linguistic rules and conventions. We show that LLMs can be generalized interfaces for both spoken and signed languages if trained with a multitasking paradigm. Our code and model checkpoints are open-source."
}