7 papers
Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation
Donghuo Zeng, Roberto Legaspi, Kazushi Ikeda
Effective persuasive dialogue agents adapt their strategies to individual users, accounting for the evolution of their psychological states and intentions throughout conversations.…
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypotheti…
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This…
Metric Learning with Progressive Self-Distillation for Audio-Visual Embedding Learning
Donghuo Zeng, Kazushi Ikeda
Metric learning projects samples into an embedded space, where similarities and dissimilarities are quantified based on their learned representations. However, existing methods oft…
Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval
Kazuaki Furumai, Roberto Legaspi, Julio Vizcarra +6
Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots employed responsibly for social good c…
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun +4
Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rel…