3 citations · 3 across the 5 of their papers we have counts for
10 papers
Concept-based explanation of gene expression prediction from H&E images
Amos Muench, Jonathan Thielmann, Reduan Achtibat +9
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods fo…
Fast & Faithful Function Vectors
Minh An Pham, Anton Segeler, Thomas Wiegand +4
Function vectors (FVs) are task representations elicited during in-context learning that can be used to steer Large Language Models (LLMs). However, design choices in their formula…
Attribution-Guided Decoding
Piotr Komorowski, Elena Golimblevskaia, Reduan Achtibat +3
The capacity of Large Language Models (LLMs) to follow complex instructions and generate factually accurate text is critical for their real-world application. However, standard dec…
Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs
Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat +5
Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagno…
The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation
Patrick Kahardipraja, Reduan Achtibat, Thomas Wiegand +2
Large language models are able to exploit in-context learning to access external knowledge beyond their training data through retrieval-augmentation. While promising, its inner wor…
Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers
Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat +3
To solve ever more complex problems, Deep Neural Networks are scaled to billions of parameters, leading to huge computational costs. An effective approach to reduce computational r…