This study presents RoleCraft-GLM, an innovative framework aimed at enhancing
personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM
addresses the key issue of lacking personalized interactions in conversational
AI, and offers a solution with detailed and emotionally nuanced character
portrayals. We contribute a unique conversational dataset that shifts from
conventional celebrity-centric characters to diverse, non-celebrity personas,
thus enhancing the realism and complexity of language modeling interactions.
Additionally, our approach includes meticulous character development, ensuring
dialogues are both realistic and emotionally resonant. The effectiveness of
RoleCraft-GLM is validated through various case studies, highlighting its
versatility and skill in different scenarios. Our framework excels in
generating dialogues that accurately reflect characters' personality traits and
emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks
a significant leap in personalized AI interactions, and paves the way for more
authentic and immersive AI-assisted role-playing experiences by enabling more
nuanced and emotionally rich dialogues

RoleCraft-GLM 是一个创新框架，旨在通过大型语言模型（LLM）提供个性化角色扮演体验。它解决了对话人工智能中缺乏个性化互动的关键问题，并通过详细和情感细腻的角色描绘提供了解决方案。通过从传统的名人角色转变为多样化的非名人角色的对话数据集，我们提供了独特的对话数据集，从而增强了语言建模交互的真实性和复杂性。此外，我们的方法包括细致的角色塑造，确保对话既真实又情感共鸣。通过多个案例研究验证了 RoleCraft-GLM 的有效性，突显了它在不同场景中的多功能和技巧。我们的框架在生成准确反映角色个性特征和情绪的对话方面表现出色，从而提高用户参与度。总之，RoleCraft-GLM 在个性化人工智能互动方面取得了重要进展，并通过实现更复杂和情感丰富的对话为更真实和沉浸式的人工智能辅助角色扮演体验铺平了道路。