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📄 Paper
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📄 Github
💬 Role-playing Model
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💬 Role-palying Evaluation Model
💬 Training Dataset
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💬 Evaluation Benchmark
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💬 Annotated Role-playing Evaluation Dataset
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💬 Human-preference Dataset
1. Introduction
This is the dataset for fine-tuning a roly-playing LLM.
More details can be seen at GitHub and Crab Homepage.
| key | value |
|---|---|
| uid | index |
| username | role name |
| botname | bot name |
| system | system prompt, including role profile |
| conversation | multi-turn conversation between user & bot |
2. Usage
from datasets import load_dataset
dataset = load_dataset("HeAAAAA/Crab-role-playing-train-set")
for k, v in dataset["train"][0].items():
print(f"{k}: {v}\n")
3. Four Datasets
We totally publish three datasets, including :
- Crab role-playing train set : the dataset used for fine‑tuning a role‑playing LLM.
- Crab role-playing evaluation benchmark :the dataset used for evalauating a role‑playing LLM.
- Manually annotated role-playing evaluation dataset: the dataset used for training a evaluator for role‑playing tasks.
- Crab Human preference dataset: the dataset used to train a role‑playing LLM via reinforcement learning
4. Fine-tuned Role-playing Model
We release a fine-tuned Role-playin LLM to achieve configurable Role-Playing tasks:
5. Role-palying Evaluation Model
We release a trained LLM to automate the evaluation of role-playing tasks:
6. Citation
@inproceedings{he2025Crab,
title={Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark},
author={Kai, He and Yucheng, Huang and Wenqing, Wang and Delong, Ran and Dongming, Sheng and Junxuan, Huang and Qika, Lin and Jiaxing, Xu and Wenqiang, Liu and Mengling, Feng},
booktitle={Proceedings of the 63nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
year={2025}
}
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