Papers by Yan Pang
Mention Extraction and Linking for SQL Query Generation (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL systems take a slot-filling approach, but they are limited in capturing inter-dependencies among SQL clauses. |
| Approach: | They propose an extraction-linking approach where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. |
| Outcome: | The proposed method achieves the first place on the WikiSQL benchmark. |
Transductive Learning for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for style transfer are based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. |
| Approach: | They propose a retrieval-based context-aware style representation that involves top-K relevant sentences in the target style in the transfer process. |
| Outcome: | The proposed method outperforms several strong baselines and is general and effective to the task of unsupervised style transfer. |
SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models (2026.acl-long)
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| Challenge: | Existing privacy-preserving inference methods sacrifice utility or efficiency, authors say . current approaches suffer a trilemma between privacy, utility, and efficiency, they say . |
| Approach: | They propose a model-agnostic framework for privacy-preserving LLM inference that reformulates privacy protection at the batch level rather than the individual-prompt level. |
| Outcome: | The proposed model-agnostic framework achieves 20% higher utility than previous models . it reduces query cost by up to 5 compared to non-batched inference . |
Improving English-Arabic Transliteration with Phonemic Memories (2022.findings-emnlp)
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| Challenge: | Existing neural approaches to transliterate names from English to Arabic are limited and focus on leveraging the phonemic association between English and Arabic. |
| Approach: | They propose a model for English-Arabic transliteration using a memory module modeling the phonemic association between English and Arabic to guide the transliterations process. |
| Outcome: | The proposed model improves on EANames corpus, which better represents names in the general public than linked Wikipedia entries that are always names of famous people. |
Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)
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| Challenge: | Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors. |
| Approach: | They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires. |
| Outcome: | The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms. |
Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on coarse-grained hallucination detection and fail to capture hallucinics . vision encoders exhibit unique hallucinian characteristics, but suboptimal of simple feature fusion. |
| Approach: | They propose a visual encoder that employs different training paradigms to instill inductive biases in visual encoded models. |
| Outcome: | The proposed system reduces hallucinations and improves model performance. |
Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)
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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng
| Challenge: | Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content. |
| Approach: | They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces . |
| Outcome: | The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining. |
FASTMATCH: Accelerating the Inference of BERT-based Text Matching (2020.coling-main)
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| Challenge: | Recent pre-trained language models have shown state-of-the-art accuracies in text matching. |
| Approach: | They propose a BERT-based text matching model where representations and interactions are decoupled . they propose generating final matching scores using a lightweight attention network . |
| Outcome: | Experiments show that the proposed model can achieve up to 100X speed-up to BERT and RoBERTa while keeping more up to 98.7% of the performance. |
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)
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WenHao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen, Jian Du, Yaxin Du, Xianghe Pang, Keduan Huang, Yanfeng Wang, Qiang Yan, Siheng Chen
| Challenge: | Existing research on Large Language Models (LLMs) relies on few servers and lacks training support. |
| Approach: | They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools. |
| Outcome: | Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work. |
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs (2024.findings-emnlp)
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| Challenge: | Obtaining large-scale, high-quality real-world fact-checking datasets is costly . generalizability of detectors trained on synthetic data to real-life scenarios remains unclear . |
| Approach: | They propose to use synthetic data to learn from real-world data to detect multimodal misinformation . they propose to combine model-agnostic data selection methods with real-life data distributions . |
| Outcome: | The proposed method improves the performance of a small MLLM on real-world fact-checking datasets, surpassing GPT-4V. |