1 citations · 2 across the 14 of their papers we have counts for
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Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Aravind Narayanan, Vahid Reza Khazaie, Shaina Raza
Large vision-language models (VLMs) can jointly interpret images and text, but they are also prone to absorbing and reproducing harmful social stereotypes when visual cues such as…
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
Ananya Raval, Aravind Narayanan, Vahid Reza Khazaie +1
Large Multimodal Models (LMMs) are typically trained on vast corpora of image-text data but are often limited in linguistic coverage, leading to biased and unfair outputs across la…
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
Shaina Raza, Aravind Narayanan, Vahid Reza Khazaie +6
Although recent large multimodal models (LMMs) show impressive progress on vision language tasks, their alignment with human centered (HC) principles such as fairness, ethics, incl…