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.
Interpretable user satisfaction estimation for conversational systems with large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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The LENS framework applied to 192 real-world settings shows moderate natural prompt distribution shifts cause 73% average performance loss in deployed LLMs, especially across user groups and regions.
WildFeedback extracts preference pairs from in-situ user feedback in LLM conversations to fine-tune models for better alignment with real user preferences.
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
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WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
WildFeedback extracts preference pairs from in-situ user feedback in LLM conversations to fine-tune models for better alignment with real user preferences.