@inproceedings{tuli-etal-2024-dynamo,
    title = "{D}yna{M}o: Accelerating Language Model Inference with Dynamic Multi-Token Sampling",
    author = "Tuli, Shikhar  and
      Lin, Chi-Heng  and
      Hsu, Yen-Chang  and
      Jha, Niraj  and
      Shen, Yilin  and
      Jin, Hongxia",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2024.naacl-long.182/",
    doi = "10.18653/v1/2024.naacl-long.182",
    pages = "3322--3345",
    abstract = "Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that reduce net inference times. Our models *dynamically* predict multiple tokens based on their confidence in the predicted joint probability distribution. We propose a lightweighttechnique to train these models, leveraging the weights of traditional autoregressive counterparts. Moreover, we propose novel ways to enhance the estimated joint probability to improve text generation quality, namely co-occurrence weighted masking and adaptive thresholding. We also propose systematic qualitative and quantitative methods to rigorously test the quality of generated text for non-autoregressive generation. One of the models in our suite, DynaMo-7.3B-T3, achieves same-quality generated text as the baseline (Pythia-6.9B) while achieving 2.57$\times$ speed-up with only 5.87{\%} and 2.67{\%} parameter and training time overheads, respectively."
}