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Computer Science > Computation and Language

arXiv:2507.11851 (cs)
[Submitted on 16 Jul 2025]

Title:Your LLM Knows the Future: Uncovering Its Multi-Token
Prediction Potential

Authors:Mohammad Samragh, Arnav Kundu, David Harrison, Kumari Nishu,
Devang Naik, Minsik Cho, Mehrdad Farajtabar
View a PDF of the paper titled Your LLM Knows the Future: Uncovering
Its Multi-Token Prediction Potential, by Mohammad Samragh and 6 other
authors
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    Abstract:Autoregressive language models are constrained by their
    inherently sequential nature, generating one token at a time.
    This paradigm limits inference speed and parallelism, especially
    during later stages of generation when the direction and
    semantics of text are relatively certain. In this work, we
    propose a novel framework that leverages the inherent knowledge
    of vanilla autoregressive language models about future tokens,
    combining techniques to realize this potential and enable
    simultaneous prediction of multiple subsequent tokens. Our
    approach introduces several key innovations: (1) a masked-input
    formulation where multiple future tokens are jointly predicted
    from a common prefix; (2) a gated LoRA formulation that preserves
    the original LLM's functionality, while equipping it for
    multi-token prediction; (3) a lightweight, learnable sampler
    module that generates coherent sequences from the predicted
    future tokens; (4) a set of auxiliary training losses, including
    a consistency loss, to enhance the coherence and accuracy of
    jointly generated tokens; and (5) a speculative generation
    strategy that expands tokens quadratically in the future while
    maintaining high fidelity. Our method achieves significant
    speedups through supervised fine-tuning on pretrained models. For
    example, it generates code and math nearly 5x faster, and
    improves general chat and knowledge tasks by almost 2.5x. These
    gains come without any loss in quality.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2507.11851 [cs.CL]
          (or arXiv:2507.11851v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2507.11851
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mohammad Samragh [view email]
[v1] Wed, 16 Jul 2025 02:31:40 UTC (2,408 KB)
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    Uncovering Its Multi-Token Prediction Potential, by Mohammad
    Samragh and 6 other authors
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