Papers by Jiahao Nie
LGSA: Label Geometry Structuring and Aligning for Hierarchical Text Classification (2026.acl-long)
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| Challenge: | Existing hierarchical text classification methods use prompt tuning or contrastive learning to implicitly learn label embeddings for classification, but this method fails to model hierarchy-aware geometric relations among labels. |
| Approach: | They propose a two-stage framework that transforms the label hierarchy from an implicit prior into an explicit embedding by using a general orthogonal frame. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on three real-world HTC datasets. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)
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| Challenge: | Named entity recognition (NER) is a key task reliant on textual data. |
| Approach: | They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries. |
| Outcome: | The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets. |