Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU. - Patrick Kahardipraja; Brielen Madureira; David Schlangen (EMNLP (1), 2021)

TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model. - Patrick Kahardipraja; Brielen Madureira; David Schlangen (ACL (Findings), 2023)

The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling. - Brielen Madureira; Patrick Kahardipraja; David Schlangen (SIGDIAL, 2023)

When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality. - Brielen Madureira; Patrick Kahardipraja; David Schlangen (ACL (1), 2024)

FADE: Why Bad Descriptions Happen to Good Features. - Bruno Puri; Aakriti Jain; Elena Golimblevskaia; Patrick Kahardipraja; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (ACL (Findings), 2025)

A Close Look at Decomposition-based XAI-Methods for Transformer Language Models. - Leila Arras; Bruno Puri; Patrick Kahardipraja; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation. - Patrick Kahardipraja; Reduan Achtibat; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2025)

Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMs. - Sayed Mohammad Vakilzadeh Hatefi; Maximilian Dreyer; Reduan Achtibat; Patrick Kahardipraja; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2025)