Human Like DPO Dataset Large Model Dialogue Fine-tuning Dataset

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Human Like DPO Dataset is a dataset designed to improve the fluency and engagement of large language model conversations. It is suitable for formats such as Direct Preference Optimization (DPO) and aims to guide the model to generate more human-like responses. The dataset covers 256 topics and contains 10,884 samples, which are distributed in multiple fields such as technology, daily life, science, history, and art.

Each sample consists of three parts: conversational questions, human-like responses, and formal responses. Conversational questions are designed to be natural and interesting, reflecting the content of daily human conversations; human-like responses are natural, conversational answers that imitate human interactions; and formal responses reflect the structure and professionalism of traditional AI responses.

The dataset can be used to fine-tune large language models to improve the coherence of conversations, reduce robotic or inhuman responses, and enhance emotional intelligence in dialogue systems. In this way, Human-Like-DPO-Dataset provides strong support for the development of more natural and humane dialogue systems.Enhancing Human-Like Responses in Large Language Models".