Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants (2024.findings-emnlp)
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| Challenge: | Existing methods to model conversational traits are costly and time consuming. |
| Approach: | They propose a method that generates diverse user profiles at decoding-time by sampling from trait-specific Language Models. |
| Outcome: | The proposed method generates diverse user profiles at decoding-time without fine-tuning. |
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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
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