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About me
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Published:
Our team from XRI Global and the University of Arizona looked at what data and AI models exist for low-resource languages. We gathered info from places like Hugging Face and GitHub to see which languages need more support.
At XRI global, I helped develop a scoring system for assessing the impact of digital divide on any low-resource language. I also built a solution for aggregating LLMs that support low-resource languages, curated a collection of benchmarks for evaluating LLM performance on low-resource languages and measured the performance of the models using the Global MMLU dataset
As part of my work for the Applied NLP course at the University of Arizona, I tested the effectiveness of lightweight language models in common sense reasoning tasks by comparing 1-step and 2-step ReAct agents using GPT-4o mini and the TextWorldExpress Commonsense environment.