Research map / Label-efficient and robust learning / Domain adaptation and generalization
Personalizing a Dialogue System With Transfer Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a personalized Q-function combining shared dialogue knowledge with user-specific preferences | PETAL · Coffee-shopping test set | average AUC | Helped |
| Transfer dialogue data from all source users instead of only similar users | Coffee-shopping dialogue system · Coffee-shopping test set | average AUC | Helped |
| Fit only on target-user data rather than transferring source knowledge | Coffee-shopping dialogue system · Coffee-shopping test set | average AUC | Hurt |
| Use PETAL's personalized policy instead of the compared baselines | PETAL · Simulated coffee-ordering | averaged reward | Helped |
| Use PETAL's personalized policy instead of the compared baselines | PETAL · Simulated coffee-ordering | averaged success rate | Helped |
| Use PETAL's personalized policy instead of the compared baselines | PETAL · Simulated coffee-ordering | averaged dialogue length | Helped |
Closest papers by meaning
- Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward (NeurIPS 2025 · similarity 0.88)
- Teaching Language Models to Evolve with Users: Dynamic Profile Modeling for Personalized Alignment (NeurIPS 2025 · similarity 0.86)
- GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems (ICLR 2022 · similarity 0.85)
- Learning Personalized End-to-End Goal-Oriented Dialog (AAAI 2019 · similarity 0.85)
- Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents (ICLR 2024 · similarity 0.84)
- Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction (AAAI 2026 · similarity 0.84)
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