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English(EN) Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG

新方法将大语言模型控制编译为更快速、更准确的分层检索增强生成

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法，用于提高大语言模型（LLM）在结构化决策流程（特别是在分层检索增强生成系统内）中作为控制器时的效率。该技术将自回归大语言模型控制编译为策略精确的并行推理过程，消除了对串行解码的需求。新方法将控制延迟显著降低了 35-47%，同时保持了高准确性，在 84-89% 的问题上与串行策略相匹配。 AI

影响 这项研究可能导致在复杂决策任务中更高效、更快速的大语言模型应用。

排序理由 该集群包含一篇详细介绍大语言模型推理新方法的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

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新方法将大语言模型控制编译为更快速、更准确的分层检索增强生成

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zehao Lin, Qianfeng Shen, Xixuan Hao, Shaobo Cui, Chunyu Li, Kai Chen, Bo Tang, Zhenjun Wang, Jin Yan, Zhiyu Li, Feiyu Xiong ·

    Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG

    arXiv:2610.30664v1 Announce Type: new Abstract: Large language models increasingly act as controllers in structured decision pipelines, yet their decisions are still executed by autoregressive text decoding. Hierarchical RAG is one such setting: at each frontier of a document tre…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Feiyu Xiong ·

    Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG

    Large language models increasingly act as controllers in structured decision pipelines, yet their decisions are still executed by autoregressive text decoding. Hierarchical RAG is one such setting: at each frontier of a document tree, an LLM commits to $b$ of $k$ sections under a…

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