5 papers
Generative Value Conflicts Reveal LLM Priorities
Andy Liu, Kshitish Ghate, Mona Diab +3
Past work seeks to align large language model (LLM)-based assistants with a target set of values, but such assistants are frequently forced to make tradeoffs between values when de…
StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection
Amal Alqahtani, Efsun Kayi, Mona Diab
The prevalence of chronic stress represents a significant public health concern, with social media platforms like Twitter serving as important venues for individuals to share their…
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…
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…
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.…