AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916783203549184 |
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| author | Cheng, Yi Liu, Wenge Xu, Kaishuai Hou, Wenjun Ouyang, Yi Leong, Chak Tou Li, Wenjie Wu, Xian Zheng, Yefeng |
| author_facet | Cheng, Yi Liu, Wenge Xu, Kaishuai Hou, Wenjun Ouyang, Yi Leong, Chak Tou Li, Wenjie Wu, Xian Zheng, Yefeng |
| contents | Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoPal: Autonomous Adaptation to Users for Personal AI Companionship Cheng, Yi Liu, Wenge Xu, Kaishuai Hou, Wenjun Ouyang, Yi Leong, Chak Tou Li, Wenjie Wu, Xian Zheng, Yefeng Computation and Language Artificial Intelligence Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship. |
| title | AutoPal: Autonomous Adaptation to Users for Personal AI Companionship |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.13960 |