5 papers · 1 filter
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility
Sambit Ghosh, Madelyn Mathai, R. Venkatesh Babu +1
Sparse autoencoders (SAEs) are typically trained on hundreds of millions to billions of activation tokens, yet little work has tested whether this scale is necessary. We study how…
GridVQA-X: A Framework for Evaluating Multimodal Explainability Methods
Sujay Belsare, Sudarshan Nikhil, Sushant Kumar +2
With the increasing development of Vision-Language Models, it becomes imperative that their predictions are readily explainable to relevant stakeholders. However, the field of expl…
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Ding Zhang, Runtao Zhou, Wenqing Zheng +3
Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node informatio…
Do Vision Language Models Need to Process Image Tokens?
Sambit Ghosh, R. Venkatesh Babu, Chirag Agarwal
Vision Language Models (VLMs) have achieved remarkable success by integrating visual encoders with large language models (LLMs). While VLMs process dense image tokens across deep t…
SynopticBench: Evaluating Vision-Language Models on Generating Weather Forecast Discussions of the Future
Timothy B. Higgins, Antonios Mamalakis, Chirag Agarwal
Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, report generation, and visual…