#Role-Playing

Explore Character-LLMs, a trainable agent designed to emulate historical and fictional characters such as Beethoven and Cleopatra. It employs Experience Reconstruction for generating diverse training data, aiming at accurate character portrayals without external prompts. Reference the EMNLP 2023 paper for method details and access released models and datasets for comprehensive AI role-playing applications.

This project employs large language models to bring anime characters to life through realistic chat interactions, accurately imitating their voices, personalities, and narratives. Featuring zero-shot role-playing with the Haruhi Suzumiya model version 0.3, it supports 32 characters with a 54,000-entry dataset and includes continuous development for ChatHaruhi 2.0. This open-source project permits commercial use, bound by relevant licensing agreements, while adhering to character and API provider compliance.

CharacterEval provides an extensive dataset featuring 1,785 dialogues and 23,020 examples to evaluate 77 characters from Chinese literature. Utilizing a multidimensional assessment system with thirteen metrics, it integrates a character-based reward model demonstrating superior human correlation compared to other models, including GPT-4. The project includes detailed annotation documents and supports consensus scoring. Additionally, it facilitates the replication of evaluation scores using multiple open-source models.

Discover a framework that improves role-playing in language models through RoleBench dataset and Context-Instruct technique. This project allows LLMs to emulate characters for complex tasks, providing over 168,000 samples for model optimization and adaptation in both proprietary and open-source settings, compatible with top AI models such as GPT-4.