Papers by Xiangbo Wu
HuatuoGPT, Towards Taming Language Model to Be a Doctor (2023.findings-emnlp)
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Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
| Challenge: | Experimental results show that the distilled language model outperforms its teacher model (ChatGPT) in most cases. |
| Approach: | They propose a Large Language Model (LLM) that leverages both distilled data from **ChatGPT** and real-world data from**doctors** in the supervised fine-tuning stage. |
| Outcome: | The proposed model outperforms the teacher model in most cases by using additional real-world data and RLMF to align the language model with the merits of both sources. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing methods for semantic incongruence in sentiment analysis are limited by label-limited settings. |
| Approach: | They propose a framework for semi-supervised multimodal sentiment analysis that emphasizes stable cross-modal representations and reliable supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under label-limited settings. |