Papers by Bing Duan
MicroC-KT: Modeling Community Effect via Learning Micro-Environment for Evidence-Grounded Explainable Knowledge Tracing (2026.acl-long)
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| Challenge: | Existing graph-based methods focus on exercise-concept relations, but lack the broader context of group references and contrastive evidence. |
| Approach: | They propose a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT by extracting contrastive group evidence. |
| Outcome: | The proposed framework outperforms state-of-the-art models on four public datasets while providing more reliable and evidence-based explanations. |
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
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| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
| Approach: | They propose a method to fine-tune a multilingual pre-trained language model fine- tuned with one (source) language and four training policies to address the performance gap. |
| Outcome: | The proposed method outperforms baselines on the XNLI dataset by a clear margin. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
From Coarse to Fine: A Multi-Granularity Multimodal Framework for Teacher Sentiment Analysis (2026.findings-acl)
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| Challenge: | Existing approaches to teacher sentiment analysis treat it as a static label . current approaches fail to capture structured heterogeneity of classroom expressions . |
| Approach: | They propose a coarse-to-fine multimodal framework that decomposes teacher sentiment into three granularities and employ CLS-guided cross-modal attention to recover effective signals from regulated displays. |
| Outcome: | The proposed framework outperforms state-of-the-art models on T-MED and CMU-MOSEI. |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
| Approach: | They propose a generative model to map natural language questions into SQL queries. |
| Outcome: | The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset. |
An Inversion Attack Against Obfuscated Embedding Matrix in Language Model Inference (2024.emnlp-main)
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| Challenge: | Recent studies have explored transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service providers. |
| Approach: | They propose to transform user inputs to obfuscated embedded vectors so that the data will not be eavesdropped by service providers. |
| Outcome: | The proposed inversion attack can recover user input 100% from the obfuscated vectors without a solid and deliberate security design and analysis . |