Homeland

The Homeland dataset is a game-based personalized dialogue dataset built to overcome the limitations of existing counterparts, such as narrow persona types and shallow dialogues. It features self-created global personas (18 structured/unstructured types with unique PIDs) from subjective and objective dimensions, with dialogues collected via random free interaction without mandatory persona use. The dataset includes high-reliability manual local persona annotations (92.02% inter-annotation consistency), plus multi-dimensional labels for emotion, topic and dialogue quality. Comparative analysis shows it has more dialogue turns, a realistic 44.00% local persona occurrence ratio, and tends to in-depth discussion of core persona types like identity, with scalable collection potential for future expansion. Here we provide some data samples.《Homeland》数据集（Homeland dataset）是一款基于游戏化框架构建的个性化对话数据集，旨在突破现有同类数据集存在的角色类型覆盖狭窄、对话深度不足等局限。该数据集涵盖从主客观双维度划分的18种结构化/非结构化类型的自研全局角色，每种角色均配有唯一标识符（PID），对话数据通过无需强制使用角色的随机自由交互方式采集获得。数据集附带高可信度的本地化角色人工标注（标注者间一致性达92.02%），并覆盖情感、主题与对话质量等多维度标签。对比分析结果显示，该数据集拥有更多对话轮次，本地化角色实际出现比例达44.00%，且更倾向于围绕身份等核心角色类型展开深度探讨，具备可拓展的后续采集扩容潜力。以下提供部分数据样本。