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LLMs improve dialogue coreference resolution with object metadata reasoning

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法，通过使大型语言模型（LLMs）能够推理对象描述和对话历史来改进基于任务的对话系统中的指代消解。该方法旨在克服当前监督模型在泛化和过拟合方面的局限性。在SIMMC 2.1数据集上的实验表明，LLMs可以通过逐步推理有效地将对话上下文与场景对象对齐，即使在少样本设置下，也能在准确性和泛化到新场景和对象方面有所提高。 AI

影响 增强了LLMs在对话系统中的推理能力，有望在复杂环境中改善用户交互和任务完成。

排序理由 这是一篇研究论文，详细介绍了一种改进对话系统指代消解的新方法。

在 arXiv cs.CL 阅读 →

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LLMs improve dialogue coreference resolution with object metadata reasoning

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Oier Ijurco, Oier Lopez de Lacalle ·

    基于对象描述的推理可改进基于任务的对话系统中的指代消解

    arXiv:2604.27850v1 Announce Type: new Abstract: Task-based dialogue systems assist users in achieving specific goals, such as executing actions or retrieving information, through natural language interactions. Accurate coreference resolution is essential, as it involves identifyi…

  2. arXiv cs.CL TIER_1 English(EN) · Oier Lopez de Lacalle ·

    基于对象描述的推理可改进基于任务的对话系统中的指代消解

    Task-based dialogue systems assist users in achieving specific goals, such as executing actions or retrieving information, through natural language interactions. Accurate coreference resolution is essential, as it involves identifying object references within the dialogue - a tas…

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