Giorgos Stamou

Optimal and efficient text counterfactuals using Graph Neural Networks

2024 · arXiv (Cornell University)

As NLP models become increasingly integral to decision-making processes, the need for explainability and interpretability has become paramount. In this work, we propose a framework that achieves the aforementioned by generating semantically edited inputs, known …

GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation

2024 · arXiv (Cornell University)

Gender bias in machine translation (MT) systems poses significant challenges that often result in the reinforcement of harmful stereotypes. Especially in the labour domain where frequently occupations are inaccurately associated with specific genders, such biases …

Conceptual Contrastive Edits in Textual and Vision-Language Retrieval

2025 · arXiv (Cornell University)

As deep learning models grow in complexity, achieving model-agnostic interpretability becomes increasingly vital. In this work, we employ post-hoc conceptual contrastive edits to expose noteworthy patterns and biases imprinted in representations of retrieval models. We …