1 citations · 1 across the 3 of their papers we have counts for
4 papers
VEAT Quantifies Implicit Associations in Text-to-Video Generator Sora and Reveals Challenges in Bias Mitigation
Yongxu Sun, Michael Saxon, Ian Yang +2
Text-to-Video (T2V) generators such as Sora raise concerns about whether generated content reflects societal bias. We extend embedding-association tests from words and images to vi…
Learning Explainable Dense Reward Shapes via Bayesian Optimization
Ryan Koo, Ian Yang, Vipul Raheja +3
Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as…
Making Large Language Models into World Models with Precondition and Effect Knowledge
Kaige Xie, Ian Yang, John Gunerli +1
World models, which encapsulate the dynamics of how actions affect environments, are foundational to the functioning of intelligent agents. In this work, we explore the potential o…
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
Louis Castricato, Alexander Havrilla, Shahbuland Matiana +5
Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control…