10 citations · 31 across the 16 of their papers we have counts for
7 papers · 1 filter
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning
Longju Bai, Angana Borah, Oana Ignat +1
Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the pr…
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…
MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews
Oana Ignat, Xiaomeng Xu, Rada Mihalcea
Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of mod…
Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data
Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea
Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effo…
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data
Shinka Mori, Oana Ignat, Andrew Lee +1
Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we nee…