Papers with PersonaChat
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)
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| Challenge: | Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent. |
| Approach: | They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency. |
| Outcome: | The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Learning From Free-Text Human Feedback – Collect New Datasets Or Extend Existing Ones? (2023.emnlp-main)
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| Challenge: | Existing datasets for learning from free-text human feedback are scarce. |
| Approach: | They manually annotate a subset of a popular dialogue dataset with error and user response types using an improved version of the Integrated Error Taxonomy and a newly proposed user response type taxonomies. |
| Outcome: | The proposed dataset provides new insights into dataset composition, error types, user response types, and the relations between them. |
Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind (2026.findings-acl)
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| Challenge: | Existing persona datasets capture only trait, and ignore impact of state. |
| Approach: | They use a Reddit dataset to study user interactions with language models . they find that existing persona datasets capture only trait and ignore impact of state . |
| Outcome: | The proposed dataset decomposes variance and finds that LLMs are state-blind . the reward models react to user state, but inconsistently, the authors say . |