Xia Hu


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Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Mengnan Du | Subhabrata Mukherjee | Yu Cheng | Milad Shokouhi | Xia Hu | Ahmed Hassan Awadallah
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks. However, very few of these studies have analyzed the impact of compression on the generalizability and robustness of compressed models for out-of-distribution (OOD) data. Towards this end, we study two popular model compression techniques including knowledge distillation and pruning and show that the compressed models are significantly less robust than their PLM counterparts on OOD test sets although they obtain similar performance on in-distribution development sets for a task. Further analysis indicates that the compressed models overfit on the shortcut samples and generalize poorly on the hard ones. We further leverage this observation to develop a regularization strategy for robust model compression based on sample uncertainty.

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Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Ruixiang Tang | Gord Lueck | Rodolfo Quispe | Huseyin Inan | Janardhan Kulkarni | Xia Hu
Findings of the Association for Computational Linguistics: EMNLP 2023

Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose information in the training data. In this study, we focus on the summarization task and investigate the membership inference (MI) attack: given a sample and black-box access to a model’s API, it is possible to determine if the sample was part of the training data. We exploit text similarity and the model’s resistance to document modifications as potential MI signals and evaluate their effectiveness on widely used datasets. Our results demonstrate that summarization models are at risk of exposing data membership, even in cases where the reference summary is not available. Furthermore, we discuss several safeguards for training summarization models to protect against MI attacks and discuss the inherent trade-off between privacy and utility.


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Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
Mengnan Du | Varun Manjunatha | Rajiv Jain | Ruchi Deshpande | Franck Dernoncourt | Jiuxiang Gu | Tong Sun | Xia Hu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.


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A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
Yan Chen | Zhoujun Li | Liqiang Nie | Xia Hu | Xiangyu Wang | Tat-Seng Chua | Xiaoming Zhang
Proceedings of COLING 2012


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Query Segmentation Based on Eigenspace Similarity
Chao Zhang | Nan Sun | Xia Hu | Tingzhu Huang | Tat-Seng Chua
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers