Papers by Longju Bai
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. |
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning (2025.naacl-long)
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| Challenge: | Large Multimodal Models exhibit impressive performance across multimodal tasks . effectiveness in cross-cultural contexts limited due to predominantly Western-centric nature of data and models . multi-agent models have shown significant capability in solving complex tasks despite limitations in crosscultural context . |
| Approach: | They propose to use a multi-agent framework to enhance cross-cultural image captioning using LMMs with distinct cultural personas to evaluate cultural information within image captions. |
| Outcome: | The proposed model outperforms single-agent models across different metrics and offers valuable insights for future research. |
Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba) (2025.findings-acl)
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Ruiqi He, Yushu He, Longju Bai, Jiarui Liu, Zhenjie Sun, Zenghao Tang, He Wang, Hanchen Xia, Rada Mihalcea, Naihao Deng
| Challenge: | Existing studies on humor in non-English languages lack culturally nuanced humor in other languages. |
| Approach: | They construct a Chinese humor explanation dataset using a reddit-like platform . they test ten LLMs and find they are significantly better than existing LLM models . |
| Outcome: | The proposed dataset is the first and largest Chinese humor explanation dataset. |