12 citations · 12 across the 4 of their papers we have counts for
5 papers
GRACE:Gradient-guided Coreset Selection for LLM Unlearning
Praveen Bushipaka, Andrea D'Angelo, Lucia Passaro +1
Machine Unlearning methods for Large Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests may provide only a few examples…
Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning
Praveen Bushipaka, Lucia Passaro, Tommaso Cucinotta
A conventional LLM Unlearning setting consists of two subsets -"forget" and "retain", with the objectives of removing the undesired knowledge from the forget set while preserving t…
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Benedetta Muscato, Lucia Passaro, Gizem Gezici +1
In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground trut…
Embracing Diversity: A Multi-Perspective Approach with Soft Labels
Benedetta Muscato, Praveen Bushipaka, Gizem Gezici +3
Prior studies show that adopting the annotation diversity shaped by different backgrounds and life experiences and incorporating them into the model learning, i.e. multi-perspectiv…
Multi-Perspective Stance Detection
Benedetta Muscato, Praveen Bushipaka, Gizem Gezici +2
Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional…