@inproceedings{
mukherjee2024optimal,
title={Optimal Design for Human Preference Elicitation},
author={Subhojyoti Mukherjee and Anusha Lalitha and Kousha Kalantari and Aniket Anand Deshmukh and Ge Liu and Yifei Ma and Branislav Kveton},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cCGWj61Ael}
}
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 efficient human preference elicitation for learning preference models. The key idea in our work is to generalize optimal designs, a methodology for computing optimal information-gathering policies, to questions with multiple answers, represented as lists of items. The policy is a distribution over lists and we elicit preferences from the list proportionally to its probability. To show the generality of our ideas, we study both absolute and ranking feedback models on items in the list. We design efficient algorithms for both and analyze them. Finally, we demonstrate that our algorithms are practical by evaluating them on existing question-answering problems.
Optimal Design for Human Preference Elicitation
Subhojyoti Mukherjee Anusha Lalitha Kousha Kalantari Aniket Anand Deshmukh Ge Liu Yifei Ma Branislav Kveton