9 citations · 16 across the 9 of their papers we have counts for
10 papers
AtlasNLP: A Country-Aware Atlas of Dataset Representation in NLP
Joan Nwatu, Tsedeniya Solomon Amare, Longju Bai +17
Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, ge…
Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG
Naihao Deng, Yilun Zhu, Joan Nwatu +2
Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs), failures persist. In this w…
The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference
Naihao Deng, Alissa Shen, Yiming Feng +5
Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. W…
Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping
Joan Nwatu, Longju Bai, Oana Ignat +1
Culture shapes the objects people use and for what purposes, yet mainstream Vision-Language (VL) datasets frequently exhibit cultural biases, disproportionately favoring higher-inc…
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…
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
Joan Nwatu, Oana Ignat, Rada Mihalcea
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the speci…
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat, Zhijing Jin, Artem Abzaliev +19
Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse t…