  In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT
and GPT4 have demonstrated immense potential in constructing open-domain
dialogue agents. However, aligning these agents with specific characters or
individuals remains a considerable challenge due to the complexities of
character representation and the lack of comprehensive annotations. In this
paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to
advance the study of dialogue agents and character alignment. The dataset
encompasses all dialogue sessions (in both English and Chinese) from the Harry
Potter series and is annotated with vital background information, including
dialogue scenes, speakers, character relationships, and attributes. These
extensive annotations may empower LLMs to unlock character-driven dialogue
capabilities. Furthermore, it can serve as a universal benchmark for evaluating
how well can a LLM aligning with a specific character. We benchmark LLMs on HPD
using both fine-tuning and in-context learning settings. Evaluation results
reveal that although there is substantial room for improvement in generating
high-quality, character-aligned responses, the proposed dataset is valuable in
guiding models toward responses that better align with the character of Harry
Potter.
