Papers by Zenglin Xu
SMARTAVE: Structured Multimodal Transformer for Product Attribute Value Extraction (2022.findings-emnlp)
Copied to clipboard
Qifan Wang, Li Yang, Jingang Wang, Jitin Krishnan, Bo Dai, Sinong Wang, Zenglin Xu, Madian Khabsa, Hao Ma
| Challenge: | Existing methods for product attribute value extraction are noisy and incomplete with missing values for most retailers. |
| Approach: | They propose a Structure Mltimodal trAnsformeR for producT Attribute Value Extraction which jointly encodes the structured product information from multiple modalities. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two multimodal product datasets. |
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities (2023.findings-eacl)
Copied to clipboard
Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei, Lizhen Qu, Gerard de Melo, Xiaojun Chang, Yazhou Ren, Zenglin Xu
| Challenge: | a vision-language benchmark for human activity planning is designed for humans . the task is easy for humans, but challenging for SOTA deep learning models . |
| Approach: | They propose a vision-language benchmark for human activity planning that extends Charades with intents and builds on a multi-choice question test set. |
| Outcome: | The proposed benchmark evaluates the ability of systems to anticipate and plan human actions in a multimodal visionlanguage setting. |
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)
Copied to clipboard
| Challenge: | a growing number of cloud-based inference services are relying on SMPC to protect data privacy. |
| Approach: | They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance. |
| Outcome: | The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE . |
Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)
Copied to clipboard
Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu
| Challenge: | Existing DRA methods fail to accurately recover the original text of real-world privacy data. |
| Approach: | They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods. |
| Outcome: | The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. |
Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews. |
| Approach: | They propose a method that relies on the category name of each aspect and a pretrained language model to generate constraints for clustering. |
| Outcome: | The proposed framework performs better than existing weakly supervised methods on nine benchmark datasets. |
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)
Copied to clipboard
| Challenge: | Existing models for response selection do not perform well when there are many candidate responses. |
| Approach: | They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response . |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks. |
MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)
Copied to clipboard
Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu
| Challenge: | Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout. |
| Approach: | They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction. |
| Outcome: | The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks. |
CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve LLMs' reasoning abilities suffer from diminishing returns or high computing overhead. |
| Approach: | They propose a genetic evolutionary framework that casts CoT generation as a population-based search over reasoning trajectories. |
| Outcome: | The proposed framework improves correct-CoT synthesis success by over 30% and enhances structural diversity with markedly improved efficiency. |
From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction. |
| Approach: | They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. |
| Outcome: | The proposed model outperforms strong baselines on the Big-Bench Hard benchmark. |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)
Copied to clipboard
| Challenge: | Named entity recognition is an essential lower-level task in natural language processing (NLP). |
| Approach: | They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines. |
| Outcome: | The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. |
Federated Model Decomposition with Private Vocabulary for Text Classification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train federated learning (FL) for natural language processing require sensitive data to leave local devices. |
| Approach: | They propose a fedrated model decomposition method that protects the privacy of vocabularies . they propose an adaptive updating technique to improve the performance of local models . |
| Outcome: | The proposed method protects the privacy of vocabularies in federated learning tasks . it maintains competitive performance and provides better privacy-preserving capacity compared to status quo methods. |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
Copied to clipboard
Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)
Copied to clipboard
Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu
| Challenge: | Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data. |
| Approach: | They propose a prompt tuning approach to extract attributes from product information using mixed prompts. |
| Outcome: | The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting. |
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing multi-agent reinforcement learning methods depend on large critic networks to evaluate joint actions, leading to instability and high memory costs. |
| Approach: | They propose a method to optimize large language models for agent-specific roles . they propose combining agent-based frameworks with retrieval-augmented generation . |
| Outcome: | Experiments show that multi-agent group policy optimization outperforms baselines in task performance and computational efficiency. |
FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP (2023.acl-long)
Copied to clipboard
| Challenge: | federated learning (FL) is a promising technique for preserving data privacy . however, there is no work on applying FL to legal NLP . |
| Approach: | They propose to use federated learning to train models in a collaborative way without sharing data . they propose to test the FL benchmark on real-world legal data from Chinese courts . |
| Outcome: | The proposed benchmark combines five legal NLP tasks and one privacy task on Chinese courts. |
APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)
Copied to clipboard
Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu
| Challenge: | Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement. |
| Approach: | They propose a method that involves tuning a small set of soft prompts for pre-trained language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark. |
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection (2024.findings-acl)
Copied to clipboard
| Challenge: | XMoE leverages small experts and a threshold-based router to selectively engage only essential parameters. |
| Approach: | They propose a novel MoE that leverages small experts to selectively engage only essential parameters. |
| Outcome: | The proposed model can reduce computation load at MoE layers by over 50% without sacrificing performance. |
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)
Copied to clipboard
Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu
| Challenge: | Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses. |
| Approach: | They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times . |
| Outcome: | CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance. |
Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing automatic question generation methods focus on encoding passage and answer to generate question. |
| Approach: | They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework. |
| Outcome: | The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks. |
Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
Copied to clipboard
Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
| Approach: | They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks . |
| Outcome: | The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education. |
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)
Copied to clipboard
Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu
| Challenge: | Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values. |
| Approach: | They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction. |
| Outcome: | The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large. |
FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing frameworks for multilingual modeling face communication costs and parameter interference conflicts. |
| Approach: | They propose a communication-efficient federated learning framework with low-rank adaptation and language family clustering for Multilingual Modeling (MM) they maintain the weights of the base model, updating the lightweight Low-rank adapt parameters to minimize communication costs. |
| Outcome: | The proposed model outperforms the baseline models in performance and reduces communication overhead. |
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost. |
| Approach: | They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel. |
| Outcome: | The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models. |
FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models often overlook key behavioral patterns underlying human financial behavior. |
| Approach: | FinHEAR is a multi-agent framework for human expertise and Adaptive Risk-aware reasoning. |
| Outcome: | FinHEAR outperforms baseline models in trend forecasting and decision-making. |