Papers by Joan C. Nwatu
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost (2024.lrec-main)
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| Challenge: | Current foundation models have shown impressive performance across various tasks, but they are not effective for everyone due to the imbalanced geographical and economic representation of the data used in the training process. |
| Approach: | They propose to identify the data to be annotated to balance model performance and annotation costs by finding countries with visual similarity for the topics. |
| Outcome: | The proposed methods improve model performance and reduce annotation costs by using data from countries with higher visual similarity for these topics. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |