58 citations · 186 across the 30 of their papers we have counts for
4 papers · 1 filter
Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration
Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1
Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of their accuracy. As attention…
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attribution Explainability
Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen +3
Deep neural network predictions are notoriously difficult to interpret. Feature attribution methods aim to explain these predictions by identifying the contribution of each input f…
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
Joakim Edin, Maria Maistro, Lars Maaløe +3
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantl…
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Joakim Edin, Alexander Junge, Jakob D. Havtorn +4
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatm…