Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929404889792512 |
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| author | Njifenjou, Ahmed Sucal, Virgile Jabaian, Bassam Lefèvre, Fabrice |
| author_facet | Njifenjou, Ahmed Sucal, Virgile Jabaian, Bassam Lefèvre, Fabrice |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18460 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation Njifenjou, Ahmed Sucal, Virgile Jabaian, Bassam Lefèvre, Fabrice Computation and Language Artificial Intelligence Human-Computer Interaction 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. |
| title | Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.18460 |