Optimal Design for Human Feedback
<|reference_start|>Optimal Design for Human Feedback: Learning of preference models from human feedback has been central to recent advances in artificial intelligence. Motivated by the cost of obtaining high-quality human annotations, we study the problem of data collection for learning preference models. The key idea in our work is to generalize the optimal design, a method for computing information gathering policies, to ranked lists. To show the generality of our ideas, we study both absolute and relative feedback on the lists. We design efficient algorithms for both settings and analyze them. We prove that our preference model estimators improve with more data and so does the ranking error under the estimators. Finally, we experiment with several synthetic and real-world datasets to show the statistical efficiency of our algorithms.<|reference_end|>
@article{mukherjee2024optimal,
    title={Optimal Design for Human Preference Elicitation},
    author={Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket
  Deshmukh, Ge Liu, Yifei Ma, and Branislav Kveton},
    journal={arXiv preprint arXiv:2404.13895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13895},
    primaryClass={cs.LG}
}