2 citations · 6 across the 16 of their papers we have counts for
6 papers · 1 filter
Probing the Representational Power of Sparse Autoencoders in Vision Models
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
Sparse Autoencoders (SAEs) have emerged as a popular tool for interpreting the hidden states of large language models (LLMs). By learning to reconstruct activations from a sparse b…
Cultural Awareness in Vision-Language Models: A Cross-Country Exploration
Avinash Madasu, Vasudev Lal, Phillip Howard
Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework…
Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
The ImageNet hierarchy provides a structured taxonomy of object categories, offering a valuable lens through which to analyze the representations learned by deep vision models. In…
Learning from Reasoning Failures via Synthetic Data Generation
Gabriela Ben Melech Stan, Estelle Aflalo, Avinash Madasu +2
Training models on synthetic data has emerged as an increasingly important strategy for improving the performance of generative AI. This approach is particularly helpful for large…
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression
Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu +6
Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpl…
Pruning the Paradox: How CLIP's Most Informative Heads Enhance Performance While Amplifying Bias
Avinash Madasu, Vasudev Lal, Phillip Howard
CLIP is one of the most popular foundation models and is heavily used for many vision-language tasks, yet little is known about its inner workings. As CLIP is increasingly deployed…