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English(EN) Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning

新方法利用内部注意力信号提升LLM情感推理能力

研究人员开发了一种名为自目标内部注意力增强(SIAB)的新方法,以帮助大型语言模型(LLM)更好地推理对话中的情感状态(如情绪)。与以往需要外部标签的方法不同,SIAB利用LLM自身的内部注意力信号来识别和增强相关的注意力头和token。该方法在ECC和GoEmotions数据集上针对多种LLM进行了评估,显示出在情感状态推理方面有显著改进,尤其是在与外部监督相结合时。 AI

影响 增强了LLM在理解和回应对话中情感细微差别方面的能力。

排序理由 详细介绍LLM情感状态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 →

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新方法利用内部注意力信号提升LLM情感推理能力

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详细介绍LLM情感状态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) · Wenqiu Tang, Zhen Wan, Yihui Wang, Takahiro Komamizu, Ichiro Ide ·

    情绪不是外部知识:用于情感状态推理的内部注意力信号

    arXiv:2610.21347v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly operate in multi-turn interactions that require maintaining latent conversational states, not only explicit facts. We study affective state as an implicit state inferred from sparse contextu…