Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization (2023.findings-acl)
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| Challenge: | Recent studies have focused on developing persona consistent dialogue models . order sensitivity affects the quality and consistency of generated response . |
| Approach: | They propose a model-agnostic framework to improve persona consistent dialogue response generation by concatenating persona texts and dialogue history as a single input sequence. |
| Outcome: | The proposed framework outperforms existing models on the Persona-Chat dataset and shows that it is more robust under different persona orders and more consistent with the persona profile. |
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| Challenge: | Personalized dialogue systems have advanced with the integration of user-specific personas into large language models (LLMs). |
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
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| Challenge: | Existing models for introducing explicit personas are expensive due to their expensive collection costs. |
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| Challenge: | Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories. |
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| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
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| Challenge: | Existing personalized dialogue systems extract user profiles from dialogue history to guide personalized response generation. |
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| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
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