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English(EN) Learning Composable Chains-of-Thought

大型语言模型探索潜在和可组合的思维链推理

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员正在探索超越标准思维链（CoT）技术来提高大型语言模型（LLM）推理能力的方法。一种方法是在“可组合 CoT”数据上训练模型，其中原子推理技能被组合起来解决未见过的任务，其表现优于多任务学习和微调。另一个研究领域侧重于检测 LLM 中的“隐藏 CoT”，提出了一个隐藏 CoT 检测分数（HCDS）来分析模型是否在没有明确中间步骤的情况下表现出潜在的推理模式。一项全面的调查还对潜在 CoT 推理的最新进展进行了分类，旨在为这一新兴范式提供结构化基础。 AI

影响 这些研究论文探索了增强 LLM 推理能力的新颖方法，有望在复杂问题解决领域带来更强大、更多功能的 AI 系统。

排序理由 该集群包含三篇 arXiv 论文，详细介绍了对 LLM 高级推理技术的研究。

在 arXiv cs.AI 阅读 →

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大型语言模型探索潜在和可组合的思维链推理

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该集群包含三篇 arXiv 论文，详细介绍了对 LLM 高级推理技术的研究。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Fangcong Yin, Zeyu Leo Liu, Liu Leqi, Xi Ye, Greg Durrett ·

    学习可组合思维链

    arXiv:2505.22635v2 Announce Type: replace-cross Abstract: A common approach for teaching large language models (LLMs) to reason is to train on chain-of-thought (CoT) traces of in-distribution reasoning problems, but such annotated data is costly to obtain for every problem of int…

  2. arXiv cs.CL TIER_1 English(EN) · Armaan Singh, Ryan Trinh Le, Jasmine Kaur, Abdullah Sultan, Edward Lue Chee Lip, Kiran Nijjer, Adnan Ahmed, Vasu Sharma ·

    利用语言、行为和机制指标检测大型语言模型中隐藏的思维链

    arXiv:2608.29956v1 Announce Type: new Abstract: Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete patterns. We propose the Hidden CoT Detection Score (HCDS), a comparative behavior…

  3. arXiv cs.CL TIER_1 English(EN) · Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen ·

    超越语言的推理：潜变量思维链推理的全面调查

    arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized intermediate steps, which constrains its broader…

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