19 citations · 44 across the 10 of their papers we have counts for
13 papers
Robustness of Neurosymbolic Reasoners on First-Order Logic Problems
Hannah Bansal, Kemal Kurniawan, Lea Frermann
Recent trends in NLP aim to improve reasoning capabilities in Large Language Models (LLMs), with key focus on generalization and robustness to variations in tasks. Counterfactual t…
Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing
Gisela Vallejo, Timothy Baldwin, Lea Frermann
The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community rece…
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing
Lea Frermann, Jiatong Li, Shima Khanehzar +1
Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on fr…
Systematic Evaluation of Predictive Fairness
Xudong Han, Aili Shen, Trevor Cohn +2
Mitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, consideri…
Optimising Equal Opportunity Fairness in Model Training
Aili Shen, Xudong Han, Trevor Cohn +2
Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing…
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
Xudong Han, Aili Shen, Yitong Li +3
This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing basel…