5 papers
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
Muhammed Saeed, Shaina Raza, Ashmal Vayani +3
Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does gramma…
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
Pardis Sadat Zahraei, Ali Emami
Addressing gender bias and maintaining logical coherence in machine translation remains challenging, particularly when translating between natural gender languages, like English, a…
Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge
Brendan Park, Madeline Janecek, Naser Ezzati-Jivan +2
Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, ap…
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries
Jing Han Sun, Ali Emami
While Large Language Models (LLMs) excel at the Winograd Schema Challenge (WSC), a coreference resolution task testing common-sense reasoning through pronoun disambiguation, they s…
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts
Pardis Sadat Zahraei, Ali Emami
The Winograd Schema Challenge (WSC) serves as a prominent benchmark for evaluating machine understanding. While Large Language Models (LLMs) excel at answering WSC questions, their…