Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation
<|reference_start|>Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation: Recently, various methods have been proposed to create open-domain conversational agents with Large Language Models (LLMs). These models are able to answer user queries, but in a one-way Q&A format rather than a true conversation. Fine-tuning on particular datasets is the usual way to modify their style to increase conversational ability, but this is expensive and usually only available in a few languages. In this study, we explore role-play zero-shot prompting as an efficient and cost-effective solution for open-domain conversation, using capable multilingual LLMs (Beeching et al., 2023) trained to obey instructions. We design a prompting system that, when combined with an instruction-following model - here Vicuna (Chiang et al., 2023) - produces conversational agents that match and even surpass fine-tuned models in human evaluation in French in two different tasks.<|reference_end|>
@article{njifenjou2024role-play,
    title={Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain
  Human-Machine Conversation},
    author={Ahmed Njifenjou, Virgile Sucal, Bassam Jabaian and Fabrice Lef`evre},
    journal={arXiv preprint arXiv:2406.18460},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18460},
    primaryClass={cs.CL cs.AI cs.HC}
}