2 citations · 6 across the 18 of their papers we have counts for
5 papers · 1 filter
From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach, William Rudman, Bastian Rieck +2
Studying the shape of text embedding spaces enhances our understanding of model behavior and how geometry affects downstream task performance. While a range of topological and geom…
APP: Accelerated Path Patching with Task-Specific Pruning
Frauke Andersen, William Rudman, Ruochen Zhang +1
Circuit discovery is a key step in many mechanistic interpretability pipelines. Current methods, such as Path Patching, are computationally expensive and have limited in-depth circ…
Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts
Michal Golovanevsky, William Rudman, Michael Lepori +3
Multimodal Large Language Models (MLLMs) perform well on tasks such as visual question answering, but it remains unclear whether their reasoning relies more on memorized world know…
TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning
Florentin Beck, William Rudman, Carsten Eickhoff
Large Language Models (LLMs) present significant computational and memory challenges due to their extensive size, making pruning essential for their efficient deployment. Existing…
Forgotten Polygons: Multimodal Large Language Models are Shape-Blind
William Rudman, Michal Golovanevsky, Amir Bar +4
Despite strong performance on vision-language tasks, Multimodal Large Language Models (MLLMs) struggle with mathematical problem-solving, with both open-source and state-of-the-art…