@inproceedings{pham-etal-2025-slimlm,
    title = "{S}lim{LM}: An Efficient Small Language Model for On-Device Document Assistance",
    author = "Pham, Thang M.  and
      Nguyen, Phat T.  and
      Yoon, Seunghyun  and
      Lai, Viet Dac  and
      Dernoncourt, Franck  and
      Bui, Trung",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.42/",
    pages = "436--447",
    ISBN = "979-8-89176-253-4",
    abstract = "While small language models (SLMs) show promises for mobile deployment, their real world performance and applications on smartphones remain underexplored. We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices. Through extensive experiments on a Samsung Galaxy S24, we identify the sweet spot between model size (ranging from 125M to 8B parameters), context length, and inference time for efficient on-device processing. SlimLM is pretrained on SlimPajama-627B and fine-tuned on DocAssist, our constructed dataset for summarization, question answering, and suggestion tasks. Our smallest model demonstrates efficient performance on S24, while larger variants offer enhanced capabilities within mobile constraints. We evaluate SlimLM against existing SLMs, showing comparable or superior performance and offering a benchmark for future research in on-device language models. We provide an Android application allowing users to experience SlimLM{'}s document assistance capabilities, offering valuable insights for mobile developers, researchers, and companies seeking privacy-preserving on-device alternatives to server-based language models."
}