19 citations · 28 across the 15 of their papers we have counts for
15 papers
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…
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…
The Quest for Reliable Metrics of Responsible AI
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quant…
Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +2
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relation…
As easy as PIE: understanding when pruning causes language models to disagree
Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1
Language Model (LM) pruning compresses the model by removing weights, nodes, or other parts of its architecture. Typically, pruning focuses on the resulting efficiency gains at the…
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance,…
Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study
Ervin Dervishaj, Tuukka Ruotsalo, Maria Maistro +1
Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by…
A Reality Check on Context Utilisation for Retrieval-Augmented Generation
Lovisa Hagström, Sara Vera Marjanović, Haeun Yu +5
Retrieval-augmented generation (RAG) helps address the limitations of parametric knowledge embedded within a language model (LM). In real world settings, retrieved information can…
DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models
Sara Vera Marjanović, Haeun Yu, Pepa Atanasova +3
Knowledge-intensive language understanding tasks require Language Models (LMs) to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated k…
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only…
Query Augmentation by Decoding Semantics from Brain Signals
Ziyi Ye, Jingtao Zhan, Qingyao Ai +4
Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved,…
Investigating the Impact of Model Instability on Explanations and Uncertainty
Sara Vera Marjanović, Isabelle Augenstein, Christina Lioma
Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are…
BrainLLM: Generative Language Decoding from Brain Recordings
Ziyi Ye, Qingyao Ai, Yiqun Liu +4
Generating human language through non-invasive brain-computer interfaces (BCIs) has the potential to unlock many applications, such as serving disabled patients and improving commu…