Papers by Qiuhao Lu
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing domain-specific pre-trained language models (PLMs) rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain- specific knowledge. |
| Approach: | They propose to integrate domain knowledge from diverse sources into PLMs by using adapters that are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller. |
| Outcome: | The proposed architecture integrates domain knowledge from diverse sources into PLMs in a parameter-efficient way. |
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization (2020.coling-main)
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Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
| Challenge: | Existing graph autoencoders and its variants have been used for node embedding . a new method is proposed to model consistency across different views of networks . |
| Approach: | They propose a network embedding method which enforces latent representations to be consistent across different views of networks by incorporating a multiview adversarial regularization module. |
| Outcome: | The proposed method compares favorably with the state-of-the-art methods on benchmark datasets and on a real-world application. |
Investigating the Multilingual Calibration Effects of Language Model Instruction Tuning (2026.eacl-short)
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Jerry Huang, Peng Lu, Qiuhao Zeng, Yusuke Iwasawa, Yutaka Matsuo, Sarath Chandar, Edison Marrese-Taylor, Irene Li
| Challenge: | despite advances in foundation model research, the relationship between large language models and their calibration remains an open area of research. |
| Approach: | They examine a gap in the calibration of large language models within multilingual settings to better understand how data scarcity can potentially lead to different calibration effects. |
| Outcome: | The proposed calibration gap is found in two multilingual benchmarks over 29 and 42 languages. |
ClinicalT5: A Generative Language Model for Clinical Text (2022.findings-emnlp)
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| Challenge: | Recent generative language models like BART and T5 are gaining popularity with their competitive performance on text generation and tasks cast as generative problems. |
| Approach: | They propose to build domain-specific PLMs through fine-tuning or pre-training from scratch over domain corpora. |
| Outcome: | The proposed model outperforms existing models on domain-specific tasks and compares favorably with its close baselines. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |