The content introduces OPEN, a framework that leverages language models and Bayesian Optimal Experimental Design to optimize user preferences. It addresses challenges in preference learning and outperforms existing methods in user studies. The framework involves featurization, initializing user preferences, selecting optimal questions, verbalizing queries, updating preferences, and prediction. Results show improved alignment with human preferences compared to LM-only approaches. The study evaluates OPEN in a content recommendation domain with 30 articles from the New York Times. User feedback highlights the importance of feature weightings and the challenges of self-mapping features to NL questions.
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by Kunal Handa,... at arxiv.org 03-11-2024
https://arxiv.org/pdf/2403.05534.pdfDeeper Inquiries