Papers by Jianwei Wang
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025.acl-long)
Copied to clipboard
| Challenge: | Existing privacy protection methods for large language models suffer from performance degradation or large inference time overhead. |
| Approach: | They propose a plug-and-play method to protect the privacy of user inputs during LLM inference . they use offline restoration vectors to train restoration vector for each privacy span type . |
| Outcome: | The proposed method can prevent the linear growth of the privacy budget. |
MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress. |
| Approach: | They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy. |
| Outcome: | The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling. |
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | federated learning (FL) fine-tunes large language models with local data, but organizations are reluctant to share local data. |
| Approach: | They propose a framework for fine-tuning large language models with local data . they propose centralized fine- tuning with local datasets is a good idea . |
| Outcome: | The proposed framework allows clients to retain local data while sharing only model parameters for training. |
Focus-Constrained Attention Mechanism for CVAE-based Response Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models generate high-frequency but trivial responses such as "I don't know" or "I'm ok" due to the discrepancy in discourse-level information, standard models generate one-to-many relationships. |
| Approach: | They propose to transform coarse-grained discourse-level information into fine-grounded word-level knowledge by introducing a fine-grain focus signal and a focus-constrained attention mechanism to take full advantage of focus. |
| Outcome: | The proposed model can generate more diverse and informative responses compared with state-of-the-art models. |
LogicST: A Logical Self-Training Framework for Document-Level Relation Extraction with Incomplete Annotations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) is difficult due to the vast number of entity pairs. |
| Approach: | They propose a neural-logic self-training framework that iteratively resolves conflicts and constructs the minimal diagnostic set for updating models. |
| Outcome: | The proposed framework outperforms existing methods on the document-level relation extraction (docRE) benchmark. |
ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning (2025.emnlp-main)
Copied to clipboard
Rui Wang, Bohao Li, Xiyang Dai, Jianwei Yang, Yi-Ling Chen, Zhen Xing, Yifan Yang, Dongdong Chen, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Existing approaches to adapt image-focused models for video understanding have not been successful in analyzing long video sequences. |
| Approach: | They propose a video instruction dataset that outperforms existing video instruction data for fine-tuning MLLMs by incrementally increasing input context length. |
| Outcome: | The proposed model outperforms existing models on video benchmarks and outperformed proprietary models on VideoMME even with a compact 7B model. |
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan
| Challenge: | Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability. |
| Approach: | They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic. |
| Outcome: | The proposed model outperforms open-source models and achieves competitive performance to closed-source model. |
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (2023.emnlp-industry)
Copied to clipboard
Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang
| Challenge: | Tabular data analysis is an important application task of large language models, but advanced models are not yet on par with expert level performance. |
| Approach: | They propose to employ Large Language Models to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. |
| Outcome: | The proposed framework is based on large language models and an automated machine learning pipeline for predictive modeling. |
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following (2023.emnlp-main)
Copied to clipboard
| Challenge: | Embodied Instruction Following has shown an impressive success rate when the environment has been seen in training, but when deployed in an unseen environment, it tends to struggle when deployed with an unsightly environment. |
| Approach: | They propose to explicitly align the agent’s hidden states with the instructions via contrastive learning to bridge the semantic gap between high-level language instructions and the agent's low-level action space. |
| Outcome: | The proposed meta-actions achieve a 4.5% success rate in unseen environments compared to a strong multi-modal Transformer baseline . |
C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots . |
| Approach: | They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information. |
| Outcome: | The proposed graph incorporates social commonsense knowledge and dialog flow information. |
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to generate RAG documents require knowledge of the target RAG system’s internal composition and implementation details, whereas black-box methods are unable to utilize interactive information. |
| Approach: | They propose a RIPRAG attack framework that treats the target RAG system as a black box and leverages a Reinforcement Learning from Black-box Feedback (RLBF) method to optimize the generation model for poisoned documents. |
| Outcome: | The proposed method achieves an attack success rate (ASR) improvement of up to 0.72 compared to baseline methods. |
RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis (2025.emnlp-main)
Copied to clipboard
Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen, Ziqian Zeng
| Challenge: | Existing solutions to fine-tune large language models for domain-specific tasks are ineffective in addressing privacy concerns. |
| Approach: | They propose a privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. |
| Outcome: | The proposed framework fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. |
Empowering Tabular Data Preparation with Language Models: Why and How? (2026.acl-long)
Copied to clipboard
Mengshi Chen, Yuxiang Sun, Tengchao Li, Jianwei Wang, Kai Wang, Xuemin Lin, Ying Zhang, Wenjie Zhang
| Challenge: | Tabular data preparation is a critical step in enhancing the usability of tabular data. |
| Approach: | They analyze how LMs can be combined with other components for different tabular data preparation tasks. |
| Outcome: | The proposed methods lack the ability to capture the relationships within tables and adapt to the tasks involved. |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
Copied to clipboard
| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism (2020.coling-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive models generate target words in parallel, but with a large latency due to the left-to-right dependency. |
| Approach: | They propose to train a conditional masked translation model and refine results within several iterations to remedy a flawed translation by non-autoregressive models. |
| Outcome: | The proposed model outperforms state-of-the-art models by over 1 BLEU while using less training computations. |
On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing zero-shot methods for information extraction use large amounts of gold standard data. |
| Approach: | They propose a framework to utilize silver data to enhance zero-shot classification methods . they propose to use off-the-shelf models of other NLP tasks to perform inference on test data . |
| Outcome: | The proposed framework outperforms baseline methods on TACRED and Wiki80 datasets by 5% and 6% on the zero-shot relation classification task and by 3% 7 % on Smile (Korean and Polish) |
Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing methods to train a parser to perform zero-shot learning are limited by the lack of training data. |
| Approach: | They propose a decomposition-based method to unify the sentence structures of questions . their method can generalize to natural questions with novel text expressions . |
| Outcome: | The proposed method improves on synthetic data and on complex web questions with novel expressions. |
ToolGate: Contract-Grounded and Verified Tool Execution for LLMs (2026.findings-acl)
Copied to clipboard
Yanming Liu, Xinyue Peng, Jiannan Cao, Xinyi Wang, Songhang Deng, Jintao Chen, Jianwei Yin, Xuhong Zhang
| Challenge: | Existing frameworks for tool-augmented LLMs rely heavily on natural language reasoning to determine when tools can be invoked and whether their results should be trusted. |
| Approach: | They propose a forward execution framework that provides logical safety guarantees and verifiable state evolution for LLM tool calling. |
| Outcome: | The proposed framework improves the reliability and verifiability of tool-augmented LLM systems while maintaining competitive performance on multi-step reasoning tasks. |
SecCoder: Towards Generalizable and Robust Secure Code Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation. |
| Approach: | They propose a generalizable and robust secure code generation method SecCoder by using in-context learning and the safe demonstration. |
| Outcome: | The proposed method achieves a significant security improvement of 7.20% on unseen test cases and better robustness against the attacked model. |