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English(EN) ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling

ULME框架统一了LLM的嵌入和生成能力

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了ULME，一个新颖的训练后框架，旨在将文本嵌入和语言生成统一在单个因果解码器LLM中。与以往将这些能力分开或并行处理的模型不同，ULME使用预测耦合来确保生成过程积极地塑造嵌入过程。这种方法在MTEB等嵌入基准测试中显示出显著的改进，同时在各种模型尺寸的标准准确性任务上保持了强大的性能。 AI

影响 这项研究通过更好地整合嵌入和生成，可能导致更高效、更有能力的LLM，从而改善检索增强生成和代理系统。

排序理由 该集群描述了一个在arXiv上发布的LLM新研究框架。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

ULME框架统一了LLM的嵌入和生成能力

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该集群描述了一个在arXiv上发布的LLM新研究框架。
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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Yang Liu, Zhiqiang Yuan, Shu-Dong Huang, Jiancheng Lv, Liwei Yang, Qiwei Yan, Kailin Lv, Jinchao Zhang ·

    ULME：用于相互增强嵌入和语言模型的预测耦合

    arXiv:2610.30705v1 Announce Type: new Abstract: Text embedding and text generation are two of the most useful capabilities of modern large language models, yet they are typically provided by separate specialized models (an embedder and a generator) trained with incompatible objec…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    ULME：用于相互增强嵌入和语言建模的预测耦合

    Text embedding and text generation are two of the most useful capabilities of modern large language models, yet they are typically provided by separate specialized models (an embedder and a generator) trained with incompatible objectives and deployed side by side in retrieval-aug…

  3. arXiv cs.CV TIER_1 English(EN) · Yuzhou Hong ·

    Autoregressive语言模型的预测潜在变量：可能性、度量与障碍

    arXiv:2610.28540v1 Announce Type: new Abstract: Autoregressive language models are trained by next-token cross-entropy, yet several recent systems also iterate a hidden state without emitting a token: continuous thoughts, pause steps, and recurrent depth. This note separates thre…

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