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概率电路提升LLM生成速度和表现力

研究人员开发了一种名为MTPC的新方法,以提高大型语言模型中多token预测的速度和表现力。该方法使用概率电路来模拟未来token的联合分布,为假设token独立性或顺序生成token的方法提供了一种更灵活的替代方案。实验表明,MTPC与推测性解码集成后,在保持原始语言模型性能的同时,显著加速了生成过程。 AI

影响 通过实现更快、更具表现力的多token预测来提高LLM的生成效率。

排序理由 该集群包含一篇学术论文,详细介绍了一种改进LLM生成的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

概率电路提升LLM生成速度和表现力

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该集群包含一篇学术论文,详细介绍了一种改进LLM生成的新方法。
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andreas Grivas, Lorenzo Loconte, Emile van Krieken, Piotr Nawrot, Yu Zhao, Euan Wielewski, Pasquale Minervini, Edoardo Ponti, Antonio Vergari ·

    Probabilistic Circuits 实现快速且富有表现力的多字节预测

    arXiv:2511.11346v2 Announce Type: replace Abstract: Multi-token prediction (MTP) is a prominent strategy to significantly speed up generation in large language models (LLMs), especially in byte-level LLMs, which are tokeniser-free but prohibitively slow. However, many existing MT…