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English(EN) Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction

新的超并行解码将 LLM 属性提取速度提高了 13.8 倍

研究人员开发了一种名为超并行解码 (HPD) 的新解码算法,可显著加快从文本中提取属性值的速度。HPD 通过操纵位置 ID 实现无序标记生成,从而能够并行处理独立序列。该方法可以将 LLM 的推理成本和时间最多降低 13.8 倍,而不会牺牲输出质量。该技术广泛适用于属性提取之外具有独立输出结构的各种任务。 AI

影响 加速具有独立输出结构的 LLM 推理,可能节省大量成本。

排序理由 介绍 LLM 新解码算法的学术论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的超并行解码将 LLM 属性提取速度提高了 13.8 倍

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介绍 LLM 新解码算法的学术论文。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani, Yilun Zhu, Shervin Malmasi ·

    打破自回归链:用于高效 LLM 属性值提取的超并行解码

    arXiv:2604.26209v1 Announce Type: new Abstract: Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive decoding is slow due to its sequential nature, the i…

  2. arXiv cs.CL TIER_1 English(EN) · Shervin Malmasi ·

    打破自回归链:用于高效 LLM 属性值提取的超并行解码

    Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive decoding is slow due to its sequential nature, the independence between output sequences offers an o…