PersonaFlow: Boosting Research Ideation with LLM-Simulated Expert Personas
<|reference_start|>PersonaFlow: Boosting Research Ideation with LLM-Simulated Expert Personas: Developing novel interdisciplinary research ideas often requires discussions and feedback from experts across different domains. However, obtaining timely inputs is challenging due to the scarce availability of domain experts. Recent advances in Large Language Model (LLM) research have suggested the feasibility of utilizing LLM-simulated expert personas to support research ideation. In this study, we introduce PersonaFlow, an LLM-based system using persona simulation to support the ideation stage of interdisciplinary scientific discovery. Our findings indicate that using multiple personas during ideation significantly enhances user-perceived quality of outcomes (e.g., relevance of critiques, creativity of research questions) without increasing cognitive load. We also found that users' persona customization interactions significantly improved their sense of control and recall of generated ideas. Based on the findings, we discuss highlighting ethical concerns, including potential over-reliance and cognitive biases, and suggest design implications for leveraging LLM-simulated expert personas to support research ideation when human expertise is inaccessible.<|reference_end|>
@article{liu2024personaflow:,
    title={PersonaFlow: Boosting Research Ideation with LLM-Simulated Expert
  Personas},
    author={Yiren Liu, Pranav Sharma, Mehul Jitendra Oswal, Haijun Xia, Yun Huang},
    journal={arXiv preprint arXiv:2409.12538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12538},
    primaryClass={cs.HC cs.AI}
}