9 citations · 16 across the 9 of their papers we have counts for
4 papers · 1 filter
Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone
Rada Mihalcea, Oana Ignat, Longju Bai +7
This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitatio…
Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Large Multi-modal Models
Joan Nwatu, Oana Ignat, Rada Mihalcea
Recent work has demonstrated that the unequal representation of cultures and socioeconomic groups in training data leads to biased Large Multi-modal (LMM) models. To improve LMM mo…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
Oana Ignat, Longju Bai, Joan Nwatu +1
Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the…