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English(EN) J-CoT: Chain-of-Thought in J-Space

J-CoT框架利用J空间坐标增强LLM推理能力

研究人员推出了一种新颖的循环推理框架J-CoT,该框架在一个由词汇索引的坐标系统“J空间”内运行。这种方法允许模型向前传递中间状态,而无需完全口头表达或在整个隐藏状态上进行递归。J-CoT旨在通过使用词汇索引系数来表示中间状态,然后将这些状态映射回模型的隐藏表示以供后续步骤使用,从而提高语言模型的推理能力。在评估中,J-CoT-Zero在数学、科学、编码和结构化路径推理任务上,性能与现有的潜在推理基线相当或更优,而J-CoT-Train则取得了最高分。 AI

影响 引入了一种新颖的框架,用于增强LLM的推理能力,超越了传统的思维链方法。

排序理由 这是一篇详细介绍改进语言模型推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

J-CoT框架利用J空间坐标增强LLM推理能力

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这是一篇详细介绍改进语言模型推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Junde Wu, Jiayuan Zhu, Fengling Liu, Minhao Hu, Jiazhen Pan ·

    J-CoT: J-Space 中的思维链

    arXiv:2607.21981v1 Announce Type: new Abstract: Chain-of-thought prompting improves language-model reasoning by carrying intermediate states across successive computation steps. However, relying on natural language as the only recurrent interface is overly restrictive, since many…