3 papers
cs.CL2025
Bridging the Gap: In-Context Learning for Modeling Human Disagreement
Benedetta Muscato, Yue Li, Gizem Gezici +2
Large Language Models (LLMs) have shown strong performance on NLP classification tasks. However, they typically rely on aggregated labels-often via majority voting-which can obscur…
cs.CL2024
Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models
Zhixue Zhao, Nikolaos Aletras
In many real natural language processing application scenarios, practitioners not only aim to maximize predictive performance but also seek faithful explanations for the model pred…
cs.CL2024
ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models
Zhixue Zhao, Boxuan Shan
Feature attribution methods (FAs), such as gradients and attention, are widely employed approaches to derive the importance of all input features to the model predictions. Existing…