Papers by Yuxin Zuo
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs (2023.findings-emnlp)
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| Challenge: | Existing studies show that multimodal machine translation systems exhibit decreased sensitivity to visual information when text inputs are complete. |
| Approach: | They propose to generate parallel VQA style pairs from source text to foster more robust cross-modal interaction. |
| Outcome: | The proposed approach generates parallel VQA style pairs from the source text, fostering more robust cross-modal interaction. |
KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)
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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency. |
| Approach: | They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task. |
| Outcome: | The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability. |
Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)
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| Challenge: | Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets. |
| Approach: | They propose an LLM-based collaborative annotation framework that refines annotations of triggers from distant supervision and carries out argument annotation. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the largest EE dataset to date . it achieves the F1 scores of 90% and 85.3% on the human-annotated test set . |