58 citations · 186 across the 30 of their papers we have counts for
5 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…
Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations
Mihaela Rotar, Theresia Veronika Rampisela, Maria Maistro
Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several deb…
Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
Ervin Dervishaj, Maria Maistro, Tuukka Ruotsalo +1
Implicit feedback -- the main data source for training Recommender Systems (RSs) -- is inherently noisy and has been shown to negatively affect recommendation effectiveness. Denois…
Measuring Individual User Fairness with User Similarity and Effectiveness Disparity
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Individual user fairness is commonly understood as treating similar users similarly. In Recommender Systems (RSs), several evaluation measures exist for quantifying individual user…
How Context Shapes Truth: Geometric Transformations of Statement-level Truth Representations in LLMs
Shivam Adarsh, Maria Maistro, Christina Lioma
Large Language Models (LLMs) often encode whether a statement is true as a vector in their residual stream activations. These vectors, also known as truth vectors, have been studie…