WasamiKirua/Human-Like-DPO-ita

Human-Like-DPO数据集是作为提高大型语言模型在对话中的流畅性和参与度的研究的一部分而创建的。该数据集适用于Direct Preference Optimization（DPO）等格式，以指导模型生成更加类似人类的响应。数据集包含了10,884个样本，分布在256个话题中，包括科技、日常生活、科学、历史和艺术等。每个样本包含一个自然引人入胜的对话问题、一个模仿人类互动的自然对话式回答，以及一个反映传统AI响应的正式回答。

The Human-Like-DPO-Dataset was created as part of research aimed at improving conversational fluency and engagement in large language models. Suitable for formats like Direct Preference Optimization (DPO) to guide models towards generating more human-like responses. The dataset includes 10,884 samples across 256 topics, such as Technology, Daily Life, Science, History, and Arts. Each sample consists of a natural and engaging conversational question, a human-like conversational answer that mimics human interaction, and a formal response reflecting traditional AI responses.