Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
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| Challenge: | Personalization is a multifaceted process that requires multiple definitions and varies between individuals. |
| Approach: | They propose to systemically survey the recent landscape of personalized dialogue generation including the datasets employed, methodologies developed, and evaluation metrics applied. |
| Outcome: | The proposed model can generate fluent and coherent responses to human queries in a language-based conversational agent. |
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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: | Personalized dialogue systems have advanced with the integration of user-specific personas into large language models (LLMs). |
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| Challenge: | Current dialogue systems fail at being engaging for users when trained end-to-end without relying on proactive reengaging scripted strategies. |
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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: | Current approaches for controlling dialogue response generation focus on high-level attributes like style, sentiment, or topic. |
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
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| Challenge: | Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios. |
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| Challenge: | Existing profiles-based dialogue datasets lack explicit profile representations or are difficult to collect. |
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Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation (2022.naacl-main)
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| Challenge: | Existing personalized dialogue systems extract user profiles from dialogue history to guide personalized response generation. |
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