SMTPO uses multi-task SFT to improve simulator feedback quality and RL with fine-grained rewards to optimize multi-turn preference reasoning in LLM-based conversational recommendation.
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AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.
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User Simulator-Guided Multi-Turn Preference Optimization for Reasoning LLM-based Conversational Recommendation
SMTPO uses multi-task SFT to improve simulator feedback quality and RL with fine-grained rewards to optimize multi-turn preference reasoning in LLM-based conversational recommendation.
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Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.