MINDECHO: Role-Playing Language Agents for Key Opinion Leaders
<|reference_start|>MINDECHO: Role-Playing Language Agents for Key Opinion Leaders: Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base. Now, there is a growing demand for RPLAs that represent Key Opinion Leaders (KOLs), \ie, Internet celebrities who shape the trends and opinions in their domains. However, research in this line remains underexplored. In this paper, we hence introduce MINDECHO, a comprehensive framework for the development and evaluation of KOL RPLAs. MINDECHO collects KOL data from Internet video transcripts in various professional fields, and synthesizes their conversations leveraging GPT-4. Then, the conversations and the transcripts are used for individualized model training and inference-time retrieval, respectively. Our evaluation covers both general dimensions (\ie, knowledge and tones) and fan-centric dimensions for KOLs. Extensive experiments validate the effectiveness of MINDECHO in developing and evaluating KOL RPLAs.<|reference_end|>
@article{xu2024mindecho:,
    title={MINDECHO: Role-Playing Language Agents for Key Opinion Leaders},
    author={Rui Xu, Dakuan Lu, Xiaoyu Tan, Xintao Wang, Siyu Yuan, Jiangjie Chen,
  Wei Chu, Yinghui Xu},
    journal={arXiv preprint arXiv:2407.05305},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05305},
    primaryClass={cs.AI}
}