1 citations · 1 across the 5 of their papers we have counts for
7 papers
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
Ruomeng Ding, Tianwei Gao, Lianrui Geng +2
Pluralistic alignment needs to accommodate diverse preferences under limited human feedback. Using AI simulators to label preference comparisons reduces annotation costs, but varia…
Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
Ruomeng Ding, Yifei Pang, He Sun +3
Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-language rubrics and validated on benchmarks…
Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions
Ruomeng Ding, Tianwei Gao, Thomas P. Zollo +3
Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocating limited questioning effort under r…
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
Zhun Deng, Thomas P Zollo, Benjamin Eyre +3
As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorit…
Improving Predictor Reliability with Selective Recalibration
Thomas P. Zollo, Zhun Deng, Jake C. Snell +2
A reliable deep learning system should be able to accurately express its confidence with respect to its predictions, a quality known as calibration. One of the most effective ways…
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas P. Zollo, Todd Morrill, Zhun Deng +3
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to s…
Distribution-Free Statistical Dispersion Control for Societal Applications
Zhun Deng, Thomas P. Zollo, Jake C. Snell +2
Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either…