3 papers
cs.SD2026
Same Words, Different Judgments: How Preferences Vary Across Modalities
Aaron Broukhim, Nadir Weibel, Eshin Jolly
Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences. However, evaluation protocols for such data were designed for…
cs.SD2025
Preference-Based Learning in Audio Applications: A Systematic Analysis
Aaron Broukhim, Yiran Shen, Prithviraj Ammanabrolu +1
Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications.…
cs.HC2025
What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence
Robert Kaufman, Aaron Broukhim, David Kirsh +1
Explanations for autonomous vehicle (AV) decisions may build trust, however, explanations can contain errors. In a simulated driving study (n = 232), we tested how AV explanation e…