C-BPO personalizes LLMs via preference-calibrated binary signals and PU learning theory to isolate inter-user differences from shared task knowledge.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=
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Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
C-BPO personalizes LLMs via preference-calibrated binary signals and PU learning theory to isolate inter-user differences from shared task knowledge.