We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)
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| Challenge: | a variety of personas can be elicited from large language models, but they are opaque and unpredictable. |
| Approach: | They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. |
| Outcome: | The proposed method captures habitual knowledge and generates persona-based responses from a large language model. |
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| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
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| Challenge: | Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diversity of human traits. |
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Guiding Variational Response Generator to Exploit Persona (2020.acl-main)
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Bowen Wu, MengYuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang
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| Challenge: | a personalized dialogue system can generate user-customized responses based on long-term memory about the user's persona. |
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Unsupervised Enrichment of Persona-grounded Dialog with Background Stories (2021.acl-short)
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| Challenge: | Existing dialog models do not contain such narratives, so we propose a gradient-based rewriting technique to enrich dialog personas with relevant background events. |
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