Papers by Yueting Zhuang

CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)

Challenge:	Recent advances in Multi-modal Large Language Models (MLLMs) introduce significant variability in data quality.
Approach:	They propose to use human and LLM preference alignment to compress large corpus of machine-generated multimodal instructions into a compact and high-quality form.
Outcome:	The proposed algorithm outperforms LLaVA-series models in MLLM benchmarks by 90% . it uses human and LLM preference alignment to compress a large dataset .

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Tao Chen, Haizhou Shi, Siliang Tang, Zhigang Chen, Fei Wu, Yueting Zhuang

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)

Challenge:	Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE)
Approach:	They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units.
Outcome:	The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods.

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Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang

Responsible & Trustworthy NLP

Challenge:	Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance.
Approach:	They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning .
Outcome:	Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance .

Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang

Information Retrieval

Challenge:	Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach:	They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome:	The proposed method is based on an industrial e-commerce benchmark and public datasets.

De-Biased Court’s View Generation with Causality (2020.emnlp-main)

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Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu

KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)

Challenge:	Existing approaches to court’s view generation can be used to address this problem, but neglecting the confounding bias in data can limit the model performance and pollute learning outcomes.
Approach:	They propose a novel Attentional and Counterfactual based Natural Language Generation method consisting of an attentional encoder and a pair of innovative counterfactual decoders to generate judgment-discriminative court's views.
Outcome:	The proposed method is able to generate judgment-discriminative court's views (both supportive and non-supportive views) under both quantitative and qualitative evaluation metrics.

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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren

Named Entity Recognition

Challenge:	Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type.
Approach:	They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions.
Outcome:	The proposed tool improves the entity typing process by linking the candidate types with some practical functions.

Video Dialog via Progressive Inference and Cross-Transformer (D19-1)

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Weike Jin, Zhou Zhao, Mao Gu, Jun Xiao, Furu Wei, Yueting Zhuang

Natural Language Interfaces

Challenge:	Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous.
Approach:	They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous.
Outcome:	The proposed method can be used to infer video dialog answers on large-scale datasets.

Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)

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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu

MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios.
Approach:	They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy.
Outcome:	The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em.

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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation.
Approach:	They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance .
Outcome:	The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Bo Chen, Xiaotao Gu, Yufeng Hu, Siliang Tang, Guoping Hu, Yueting Zhuang, Xiang Ren

Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)

Challenge:	Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself.
Approach:	They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet.
Outcome:	The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin.

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Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He

Reasoning

Challenge:	Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model .
Approach:	They propose to transfer knowledge from discourse markers to augment the quality of the NLI model.
Outcome:	The proposed method achieves state-of-the-art performance on large-scale datasets.

Natural Language Video Localization with Learnable Moment Proposals (2021.emnlp-main)

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Shaoning Xiao, Long Chen, Jian Shao, Yueting Zhuang, Jun Xiao

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

Challenge:	Existing methods for video moment localization have poor performance due to predefined rules.
Approach:	They propose a model with a fixed set of learnable moment proposals with 'border-aware loss' they propose to localize the video moment corresponding to the query by locating the start and end timestamps in an untrimmed video.
Outcome:	The proposed model outperforms state-of-the-art models on two challenging benchmarks.

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Information Retrieval

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text (2024.acl-long)

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Aoxiong Yin, Haoyuan Li, Kai Shen, Siliang Tang, Yueting Zhuang

Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)

Challenge:	Existing vector quantization methods are fixed-length encodings, overlooking the uneven information density in sign language.
Approach:	They propose a two-stage sign language production paradigm that encodes sign language sequences into discrete codes and autoregressively generates sign languages from text.
Outcome:	The proposed model can dynamically adjust the encoding length based on the information density in sign language to achieve accurate and compact encoded enccoding.

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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang

Green & Sustainable NLP

Challenge:	Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant.
Approach:	They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs.
Outcome:	The proposed method improves performance on a range of natural language processing tasks.

Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)

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Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

Challenge:	Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections.
Approach:	They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step .
Outcome:	The proposed method achieves state-of-the-art on five widely used benchmarks.

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Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang

Low-Resource NLP

Challenge:	Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates.
Approach:	They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities.
Outcome:	The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism .

AHEAD: Attention Head Energy-Aware Dynamics for Hallucination Mitigation in MLLMs (2026.findings-acl)

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Jiale Chang, Ying Li, Siliang Tang, Yueting Zhuang

Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)

Challenge:	Existing approaches to hallucination mitigation ignore heterogeneous behaviors of attention heads . hallucinosity is a critical barrier to multimodal large language models' reliability, authors say .
Approach:	They propose a framework that quantifies the energetic properties of each attention head during object generation through two potential networks and dynamically adjusts their contributions at inference time.
Outcome:	The proposed framework reduces hallucination rates without fine-tuning the base model while maintaining generation quality.

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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang

Knowledge Representation

Challenge:	Named entity recognition is a fundamental task in natural language processing.
Approach:	They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner.
Outcome:	The proposed method outperforms existing state-of-the-art models on nested and flat datasets.

Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)

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Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang

Natural Language Interfaces

Challenge:	Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored.
Approach:	They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks.
Outcome:	The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively.

Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)

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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren

Knowledge Representation

Challenge:	Existing collective entity linking methods are expensive and often lack local context information.
Approach:	They propose a dynamic context-augmented inference model that can be used to make collective inference.
Outcome:	The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms.

Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)

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Qi Zhang, Siliang Tang, Xiang Ren, Fei Wu, Shiliang Pu, Yueting Zhuang

Green & Sustainable NLP

Challenge:	Existing methods to train unbiased methods such as REINFORCE take time to train.
Approach:	They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets.
Outcome:	The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training.

Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024.acl-long)

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Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Recent research indicates without external feedback, LLM’s intrinsic reflection is unstable.
Approach:	They propose a method that combines self-evaluated and external feedback to improve LLM's reflection.
Outcome:	The proposed method improves the quality of self-evaluated feedback and can catalyze more accurate and stable reflection.

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Reasoning

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)

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Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang

Challenge:	Existing methods to measure scholarly impact of documents without citations only consider word frequency change.
Approach:	They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts.
Outcome:	The proposed model outperforms existing models on document influence evaluation without citations.

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)

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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang