Publications (51)
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Robert Morabito, Sangmitra Madhusudan, Tyler McDonald +1
Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies…
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…
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
Mahdi Ahmadi, Ali Emami, Mohsen Hajabdollahi +4
By increasing the volume of telemedicine information, the need for medical image compression has become more important. In angiographic images, a small ratio of the entire image us…
Common to Whom? Regional Cultural Commonsense and LLM Bias in India
Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno +3
Existing cultural commonsense benchmarks treat nations as monolithic, assuming uniform practices within national boundaries. But does cultural commonsense hold uniformly within a n…
Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks
Sarfaroz Yunusov, Kaige Chen, Kazi Nishat Anwar +1
As Large Language Models (LLMs) increasingly integrate into everyday workflows, where users shape outcomes through multi-turn collaboration, a critical question emerges: do users w…
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models
Robert Morabito, Jad Kabbara, Ali Emami
Debiasing methods that seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text have recently gained traction. In this paper, we pr…