2 citations · 2 across the 4 of their papers we have counts for
4 papers
EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences
Kshitish Ghate, Andy Liu, Devansh Jain +5
As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We…
Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes
Kshitish Ghate, Tessa Charlesworth, Mona Diab +1
To build fair AI systems we need to understand how social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifest in biases in downstream tasks.…
Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders
Kshitish Ghate, Isaac Slaughter, Kyra Wilson +2
While recent work has found that vision-language models trained under the Contrastive Language Image Pre-training (CLIP) framework contain intrinsic social biases, the extent to wh…
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition
Isaac Slaughter, Craig Greenberg, Reva Schwartz +1
Previous work has established that a person's demographics and speech style affect how well speech processing models perform for them. But where does this bias come from? In this w…