Enabling Real-Time Conversations with Minimal Training Costs
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929503696060416 |
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| author | Xu, Wang Wang, Shuo Zhao, Weilin Han, Xu Yan, Yukun Zhang, Yudi Tao, Zhe Liu, Zhiyuan Che, Wanxiang |
| author_facet | Xu, Wang Wang, Shuo Zhao, Weilin Han, Xu Yan, Yukun Zhang, Yudi Tao, Zhe Liu, Zhiyuan Che, Wanxiang |
| contents | Large language models (LLMs) have demonstrated the ability to improve human efficiency through conversational interactions. Conventional LLM-powered dialogue systems, operating on a turn-based paradigm, preclude real-time interaction during response generation. To address this limitation, researchers have proposed duplex models. These models can dynamically adapt to user input, facilitating real-time interactive feedback. However, these methods typically require substantial computational resources to acquire the ability. To reduce overhead, this paper presents a new duplex decoding approach that enhances LLMs with duplex ability, requiring minimal additional training. Specifically, our method employs parallel decoding of queries and responses in conversations, effectively implementing a channel-division-multiplexing decoding strategy. Experimental results indicate that our proposed method significantly enhances the naturalness and human-likeness of user-AI interactions with minimal training costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Real-Time Conversations with Minimal Training Costs Xu, Wang Wang, Shuo Zhao, Weilin Han, Xu Yan, Yukun Zhang, Yudi Tao, Zhe Liu, Zhiyuan Che, Wanxiang Computation and Language Large language models (LLMs) have demonstrated the ability to improve human efficiency through conversational interactions. Conventional LLM-powered dialogue systems, operating on a turn-based paradigm, preclude real-time interaction during response generation. To address this limitation, researchers have proposed duplex models. These models can dynamically adapt to user input, facilitating real-time interactive feedback. However, these methods typically require substantial computational resources to acquire the ability. To reduce overhead, this paper presents a new duplex decoding approach that enhances LLMs with duplex ability, requiring minimal additional training. Specifically, our method employs parallel decoding of queries and responses in conversations, effectively implementing a channel-division-multiplexing decoding strategy. Experimental results indicate that our proposed method significantly enhances the naturalness and human-likeness of user-AI interactions with minimal training costs. |
| title | Enabling Real-Time Conversations with Minimal Training Costs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.11727 |