Papers by Xiaoqing Xu
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
| Challenge: | Existing methods to defend against adversarial word-substitution attacks have not been evaluated or compared in a systematic manner. |
| Approach: | They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization (2025.findings-emnlp)
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Zhibo Xu, Zhu JianHao, Jingwen Xu, Changze Lv, Zhenghua Wang, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | federated learning approaches are limited by the complexity of large language models and the need for specialized expertise to protect intellectual property. |
| Approach: | They propose a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters. |
| Outcome: | The proposed approach maintains strong model performance in federated learning settings and achieves enhanced protection of model parameters compared to baseline methods. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
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| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
| Outcome: | The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. |
Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts (2023.findings-emnlp)
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Muling Wu, Wenhao Liu, Jianhan Xu, Changze Lv, Zixuan Ling, Tianlong Li, Longtao Huang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Prompt tuning has been proven to be successful on various tasks by incorporating a small number of trainable parameters while freezing large pre-trained language models. |
| Approach: | They propose a token-wise prompt tuning method that uses a bank of finer-grained soft prompt tokens to generate an instance-dependent prompt. |
| Outcome: | The proposed method performs far better than full parameter fine-tuned models and achieves state-of-the-art by tuning only 0.035% parameters on 14 datasets. |
Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback (2025.emnlp-industry)
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| Challenge: | Using a mixtral-87B model, we reduce the time it takes to generate concise notes during conversations, and reduce the amount of time spent on manual review. |
| Approach: | They propose a system that combines a Mixtral-87B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. |
| Outcome: | The proposed system achieved a 3% reduction in case handling time compared to bulk summarization, and high agent satisfaction ratings from surveys. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost. |
| Approach: | They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages . |
| Outcome: | The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%. |
Weight Perturbation as Defense against Adversarial Word Substitutions (2022.findings-emnlp)
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| Challenge: | Existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. |
| Approach: | They propose to perform weight perturbations in the parameter space rather than the input feature space to improve adversarial robustness of NLP models. |
| Outcome: | The proposed method improves adversarial robustness of models by performing weight perturbations in the parameter space rather than the input feature space. |
Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)
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Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer
| Challenge: | Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown. |
| Approach: | They propose an approach to edit and re-align alt-texts associated with images using human annotation. |
| Outcome: | The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks. |
Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation (2025.acl-long)
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| Challenge: | Existing methods for enhancing recommendation quality face false negatives . only one "silly cop movie" is labeled as positive, leading to suboptimal recommendations . |
| Approach: | They propose a data augmentation framework that leverages an LLM-based semantic retriever to identify diverse and semantically relevant items and filter them by a relevance scorer to remove noisy candidates. |
| Outcome: | The proposed approach improves performance on two benchmark datasets and user simulators. |
Generating Responses with a Specific Emotion in Dialog (P19-1)
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| Challenge: | EmoDS can express emotions in both ways, but it is difficult to scale to large datasets. |
| Approach: | They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions . |
| Outcome: | The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression. |
Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations (2023.findings-acl)
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| Challenge: | Existing methods to learn models on corpus of pairs of sentences require labor-intensive annotation. |
| Approach: | They propose to leverage distributed contextual word and phrase representations pre-trained on unlabelled texts to deal with homonymy and polysemy. |
| Outcome: | The proposed model achieves better accuracy on question-answering and relation extraction tasks. |
Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026.findings-acl)
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Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng
| Challenge: | Existing methods to address the "lost-in-the-middle" problem suffer from high latency or suboptimal hand-crafted scaling strategies. |
| Approach: | They propose a layer-specific positional embedding scaling method that assigns distinct scaling factors to each layer. |
| Outcome: | Experiments show that the proposed method mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks. |
Watermarking PLMs on Classification Tasks by Combining Contrastive Learning with Weight Perturbation (2023.findings-emnlp)
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Chenxi Gu, Xiaoqing Zheng, Jianhan Xu, Muling Wu, Cenyuan Zhang, Chengsong Huang, Hua Cai, Xuanjing Huang
| Challenge: | Large pre-trained language models (PLMs) are highly valuable intellectual property due to their expensive training costs. |
| Approach: | They propose to embed backdoors that can be triggered by specific inputs into models by model watermarking. |
| Outcome: | The proposed method can be used to protect the intellectual property of large pre-trained language models without knowledge about downstream tasks. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022.findings-acl)
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| Challenge: | Existing defense methods improve the adversarial robustness by making models adapt to training set augmented with some adversarials. |
| Approach: | They propose to introduce a reweighting mechanism to calibrate the training distribution to obtain robust models. |
| Outcome: | The proposed method minimizes the loss of validation set mixed with clean examples and adversarial ones in an online learning manner. |
Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human–Agent Interaction (2026.acl-long)
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Zisu Huang, Muzhao Tian, Xiaohua Wang, Jingwen Xu, Zhengkang Guo, Qi Qian, Kaitao Song, Jiakang Yuan, Changze Lv, Xiaoqing Zheng
| Challenge: | Existing systems that use memory as an "all-or-nothing" approach to memory usage are often static and rely on experience-following tendencies. |
| Approach: | They propose a framework that allows users to dynamically regulate memory reliance by adding context into the model's prompt. |
| Outcome: | The proposed model outperforms prompting and memory masking strategies in multiple scenarios. |