PCoKG：具有辩论机制的个性感知常识推理
PCoKG: Personality-aware Commonsense Reasoning with Debate
AI总结：
PCoKG通过引入辩论机制构建个性感知常识知识图谱，提升对话生成的一致性与个性化水平。
摘要：
大多数常识推理模型忽视了个性特质的影响，限制了其在对话生成等个性化系统中的有效性。为了解决这一限制，我们引入了具有个性感知的常识知识图谱（PCoKG），它包含521,316个四元组。我们首先使用三个评估者对ATOMIC数据集中的事件进行评分和过滤，选择那些可能引发不同个性类型下多样化推理模式的事件。在知识图谱构建中，我们利用大语言模型（LLMs）的角色扮演能力进行推理任务。为了提高生成知识的质量，我们引入了由倡导者、反对者和裁判组成的辩论机制，通过反馈循环迭代优化输出。我们从多个角度评估了该数据集，并使用多种LLM基础模型进行微调和消融实验，以评估PCoKG的鲁棒性和其构建流程的有效性。我们的LoRA微调结果表明，模型性能与基础模型的参数规模之间存在正相关性。最后，我们将PCoKG应用于基于人设的对话生成，其中它展示了生成响应与参考输出之间的一致性得到改善。这项工作弥合了常识推理与个体认知差异之间的差距，使开发更加个性化和情境感知的AI系统成为可能。
英文摘要：
Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structured dataset comprising 521,316 quadruples. We begin by employing three evaluators to score and filter events from the ATOMIC dataset, selecting those that are likely to elicit diverse reasoning patterns across different personality types. For knowledge graph construction, we leverage the role-playing capabilities of large language models (LLMs) to perform reasoning tasks. To enhance the quality of the generated knowledge, we incorporate a debate mechanism consisting of a proponent, an opponent, and a judge, which iteratively refines the outputs through feedback loops. We evaluate the dataset from multiple perspectives and conduct fine-tuning and ablation experiments using multiple LLM backbones to assess PCoKG's robustness and the effectiveness of its construction pipeline. Our LoRA-based fine-tuning results indicate a positive correlation between model performance and the parameter scale of the base models. Finally, we apply PCoKG to persona-based dialogue generation, where it demonstrates improved consistency between generated responses and reference outputs. This work bridges the gap between commonsense reasoning and individual cognitive differences, enabling the development of more personalized and context-aware AI systems.
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