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REIGN模型提供高效长文档检索，训练成本更低 · 跟踪2个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员推出REIGN（Refurbished Embeddings with Integrated Guidance Networks），这是一种新颖的双编码器，用于在长文档上进行高效的密集检索。与传统的二次方扩展的token级编码器不同，REIGN处理来自冻结的Guidance Network的上下文块嵌入，将训练成本显著降低了约四个数量级。这种方法将token处理与文档级推理解耦，并允许缓存，使其在文档到文档的检索任务中更高效。REIGN在包括Wikipedia、LoCo套件和专利检索在内的各种基准测试中，表现与更大的模型相当。 AI

影响 这项研究可以显著提高处理和检索非常长文档中信息的可行性和成本效益，影响法律科技、科学研究和知识管理等领域。

排序理由 该集群包含一篇研究论文，详细介绍了用于密集检索中高效上下文长度扩展的新模型和方法。

在 arXiv cs.IR (Information Retrieval) 阅读 →

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REIGN模型提供高效长文档检索，训练成本更低 · 跟踪2个来源

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该集群包含一篇研究论文，详细介绍了用于密集检索中高效上下文长度扩展的新模型和方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Devrim \c{C}avu\c{s}o\u{g}lu, Emre Akba\c{s} ·

    REIGN：集成引导网络的翻新嵌入，用于高效的上下文长度扩展

    arXiv:2608.29899v1 Announce Type: cross Abstract: Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K tokens only through architectural workarounds or by stretching billion…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Emre Akbaş ·

    REIGN：集成引导网络的翻新嵌入，用于高效的上下文长度扩展

    Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K tokens only through architectural workarounds or by stretching billion-parameter LLMs. We propose REIGN (Refurbished Emb…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Emre Akbaş ·

    REIGN：集成引导网络的翻新嵌入，用于高效的上下文长度扩展

    Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K tokens only through architectural workarounds or by stretching billion-parameter LLMs. We propose REIGN (Refurbished Emb…

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