Papers by Min Yu

CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization (2023.acl-long)

Challenge:	Large reasoning models have performance enhancements but still suffer from shortcomings due to limitations of the underlying language models.
Approach:	They propose a framework that allows the model to choose when to trust or ignore the tool results based on the confidence score of generated code blocks.
Outcome:	The proposed framework reduces the "Tool Ignored" issue by 4.1% to 7.5%.

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Min Xiao, Junnan Zhu, Haitao Lin, Yu Zhou, Chengqing Zong

SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)

Challenge:	Existing multimodal summarization models ignore the contribution of visual modalities . we propose a novel contribution network to consider different contributions of images .
Approach:	They propose a Coarse-to-Fine contribution network for multimodal summarization to consider different contributions of images for summarizing.
Outcome:	The proposed system outperforms baselines on the visual and textual modalities.

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Ran Liu, Ming Liu, Min Yu, He Zhang, Jianguo Jiang, Gang Li, Weiqing Huang

Text Generation

Challenge:	a growing need for long document summarization datasets with 16k input is causing problems.
Approach:	They propose to use a dataset to analyze salient information in long document summarizations.
Outcome:	The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality.

Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)

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Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

Challenge:	despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers .
Approach:	They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel.
Outcome:	The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence .

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

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Tengfei Yu, Xuebo Liu, Zhiyi Hou, Liang Ding, Dacheng Tao, Min Zhang

Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games (2025.acl-long)

Challenge:	Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks.
Approach:	They propose a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning.
Outcome:	The proposed model mitigates speech anchor bias and improves the fusion of speech and text modalities in large language models.

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Seungwon Lim, Seungbeen Lee, Dongjun Min, Youngjae Yu

Challenge:	Text-based interactive environments have long presented formidable challenges for AI.
Approach:	They propose a method for projecting human personality traits onto agents to guide their behavior and integrate them into their policy-learning pipelines.
Outcome:	The proposed method induces personality in a text-based game agent by integrating personality profiles directly into the agent's policy-learning pipeline.

ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination (2023.findings-emnlp)

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Dongfang Li, Jindi Yu, Baotian Hu, Zhenran Xu, Min Zhang

Explainability & Interpretability in NLP

Challenge:	Existing explanation datasets for large language models are limited to the English language and general domain, leading to a scarcity of linguistic diversity and a lack of resources in specialized domains, such as medical.
Approach:	They propose to use a medical dataset to assess the interpretability of Large Language Models (LLMs) . they propose to analyze medical text and generate rationales for their decisions .
Outcome:	The proposed model passes the pharmacist examination with a 75.7% accuracy, while other models like ChatGPT fail.

Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning (2026.findings-acl)

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Jun Rao, Xuebo Liu, Hexuan Deng, Zepeng Lin, Zixiong Yu, Jiansheng Wei, Xiaojun Meng, Min Zhang

CREPE: Open-Domain Question Answering with False Presuppositions (2023.acl-long)

Challenge:	Current data selection paradigms rely on static, externally defined metrics, which fail to adapt to the evolving capabilities of models during training.
Approach:	They propose a dynamic sampling framework that aligns training data with the model's intrinsic competence by iterating on real-time feedback.
Outcome:	Extensive experiments on eight benchmarks show that SAI-DPO outperforms static baselines at most nearly 6 points, achieving state-of-the-art efficiency with significantly less data.

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Xinyan Yu, Sewon Min, Luke Zettlemoyer, Hannaneh Hajishirzi

Natural Language Interfaces

Challenge:	Existing question answering datasets assume all questions have well defined answers.
Approach:	They propose a QA dataset containing a distribution of false presuppositions . they find that 25% of questions contain false presumptions .
Outcome:	The proposed model finds that 25% of questions contain false presuppositions . the model can find presuffpositions moderately well, but struggle when predicting correctness .

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

Sentiment Analysis

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)

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Yingjie Zhu, Xuefeng Bai, Kehai Chen, Yang Xiang, Jun Yu, Min Zhang

CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)

Challenge:	Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains.
Approach:	They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks.
Outcome:	Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information.

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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang

Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation (2022.emnlp-main)

Challenge:	End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data.
Approach:	They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances.
Outcome:	The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets.

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Yu Zhao, Jianguo Wei, ZhiChao Lin, Yueheng Sun, Meishan Zhang, Min Zhang

Using active learning to expand training data for implicit discourse relation recognition (D18-1)

Challenge:	Image-to-text tasks such as captioning and controllable image descriptions have received extensive attention for decades.
Approach:	They propose a new perspective for image-to-text to generate spatial descriptions by combining two objects in an image.
Outcome:	The proposed model is awe-inspiring and human-like, and the proposed end-to-end architecture is the better choice for their integration.

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Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, Guodong Zhou

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)

Challenge:	Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition.
Approach:	They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives.
Outcome:	The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives.

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Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang

Dependency-aware Prototype Learning for Few-shot Relation Classification (2022.coling-1)

Challenge:	Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries.
Approach:	They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph .
Outcome:	The proposed model can parse 669/252 sentences per second without and with pre-trained models.

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Tianshu Yu, Min Yang, Xiaoyan Zhao

Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)

Challenge:	Existing methods for few-shot relation classification fail to distinguish multiple relations that co-exist in one sentence.
Approach:	They propose a dependency-aware prototype learning method for few-shot relation classification . they utilize dependency trees and shortest dependency paths as structural information .
Outcome:	The proposed method achieves better performance than baselines on the FewRel dataset.

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Junjie Yu, Tong Zhu, Wenliang Chen, Wei Zhang, Min Zhang

Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)

Challenge:	Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models.
Approach:	They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data.
Outcome:	The proposed model improves performance even on a strong baseline.

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Liangxin Liu, Xuebo Liu, Lian Lian, Shengjun Cheng, Jun Rao, Tengfei Yu, Hexuan Deng, Min Zhang

Text Generation

Challenge:	Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models.
Approach:	They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features.
Outcome:	The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks.

Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)

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Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, Dong Yu

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck.
Approach:	They propose a framework that uses a generative verifier to provide soft, probabilistic rewards.
Outcome:	The proposed framework outperforms existing models up to 10x their size and can be scalable and effective.

TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)

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Junnan Zhu, Min Xiao, Yining Wang, Feifei Zhai, Yu Zhou, Chengqing Zong

Information Retrieval

Challenge:	Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability.
Approach:	They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs.
Outcome:	The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese .

CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)

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Haosi Mo, Xinyu Ma, Xuebo Liu, Derek F. Wong, Yu Li, Jie Liu, Min Zhang

Language Models

Challenge:	Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.
Approach:	They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions.
Outcome:	The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks.

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Jinlong Ma, Yu Zhang, Xuefeng Bai, Kehai Chen, Yuwei Wang, Zeming Liu, Jun Yu, Min Zhang

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)

Challenge:	Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification.
Approach:	They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization.
Outcome:	The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization.

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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)

Challenge:	Foundational models and their checkpoints have advanced deep learning, boosting performance across applications.
Approach:	They propose a method for pruning fine-tuned models by calculating differences between them and original model.
Outcome:	The proposed method can improve performance across vision, NLP, and multi-modal benchmarks.

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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li

Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning (2020.coling-main)

Challenge:	Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks.
Approach:	They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task .
Outcome:	The proposed model is superior in learning speech-text alignment and multi-turn dialog context.

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Chunpu Xu, Yu Li, Chengming Li, Xiang Ao, Min Yang, Jinwen Tian

Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)

Challenge:	Existing approaches to image paragraph captioning ignore the past alignment information, resulting in repetitive captioning and incomplete captioning.
Approach:	They propose an Interactive key-value Memory-augmented Attention model for image paragraph captioning to keep track of attention history along with update-chain of decoder state.
Outcome:	Extensive experiments on a benchmark dataset demonstrate the effectiveness of the proposed model.

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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang

Challenge:	Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance.
Approach:	They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively.
Outcome:	The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark.

sudo rm -rf agentic_security (2025.acl-industry)

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Sejin Lee, Jian Kim, Haon Park, Ashkan Yousefpour, Sangyoon Yu, Min Song

Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly used as computer-use agents . authors present a novel attack framework that bypasses refusal-trained safeguards .
Approach:	They propose a new attack framework that bypasses refusal-trained safeguards in LLMs . SUDO iteratively refines its attacks based on a built-in refusal feedback . authors highlight need for robust, context-aware safeguards if LLM is to be used .
Outcome:	The proposed framework bypasses refusal-trained safeguards in commercial agents . it achieves a stark attack success rate of 24.41% (with no refinement) and up to 41.33% (by iterative refinement).

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Yejin Son, Saejin Kim, Dongjun Min, Youngjae Yu

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)

Challenge:	Multimodal UNcommonsense (MUN) is a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations.
Approach:	They propose a retrieval-based in-context learning framework that transfers reasoning capabilities from larger models to smaller ones without additional training.
Outcome:	The proposed method improves on baseline ICL methods by 8.3% over previous methods.

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Jun Rao, Zepeng Lin, Xuebo Liu, Xiaopeng Ke, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory (2024.findings-emnlp)

Challenge:	Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities.
Approach:	They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base.
Outcome:	The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods.

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Suyeon Lee, Sunghwan Kim, Minju Kim, Dongjin Kang, Dongil Yang, Harim Kim, Minseok Kang, Dayi Jung, Min Kim, Seungbeen Lee, Kyong-Mee Chung, Youngjae Yu, Dongha Lee, Jinyoung Yeo

Linguistics & Cognitive NLP

Challenge:	Existing models that use large language models are not available due to ethical concerns, and data privacy concerns are a concern.
Approach:	They propose a multi-turn dialogue dataset that emulates real-life counseling interactions using the goal-oriented approach of Cognitive Behavioral Therapy (CBT).
Outcome:	The proposed model outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.

Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)

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Xuebo Liu, Yutong Wang, Derek F. Wong, Runzhe Zhan, Liangxuan Yu, Min Zhang

UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)

Challenge:	CR is the ability to understand and navigate the world using basic knowledge and understanding shared by most people.
Approach:	They propose to incorporate pretrained knowledge into NMT models and use them as robust testbeds for investigating CR in NMT.
Outcome:	The proposed method improves the training of NMT models with high CR abilities and provides accurate evaluation metrics.

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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

Challenge:	Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation.
Approach:	They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances .
Outcome:	The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks.

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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models (2025.coling-main)

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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Weicheng Li, Lixin Zou, Min Tang, Qing Yu, Wanli Li, Chenliang Li

Language Models

Challenge:	Supervised fine-tuning (SFT) is widely adopted for tailoring large language models (LLMs) to specific downstream tasks.
Approach:	They propose a memory-efficient method for automatic sample reweighting that learns to re-weight fine-tuning samples by minimizing the loss on a small, high-quality validation set.
Outcome:	Meta-LoRA learns to reweight fine-tuning samples by minimizing the loss on a small, high-quality validation set through an end-to-end bi-level optimization framework based on meta-learning.

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

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Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Dialogue Systems & Conversational Agents

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

Efficient Second-Order TreeCRF for Neural Dependency Parsing (2020.acl-main)

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Yu Zhang, Zhenghua Li, Min Zhang

Green & Sustainable NLP

Challenge:	In the deep learning (DL) era, dependency parsing models are extremely simplified with little hurt on performance thanks to the remarkable capability of multi-layer BiLSTMs in context representation.
Approach:	They propose to extend the biaffine parser to a second-order TreeCRF extension to reduce the complexity of the inside-outside algorithm.
Outcome:	The proposed extension can be used to batchify the inside and Viterbi algorithms and avoid the complex outside algorithm via efficient back-propagation.

Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)

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Yu Zhang, Kehai Chen, Xuefeng Bai, Zhao Kang, Quanjiang Guo, Min Zhang

Structured Data in NLP

Challenge:	Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph.
Approach:	They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge.
Outcome:	The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge.

PromptST: Abstract Prompt Learning for End-to-End Speech Translation (2023.emnlp-main)

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Tengfei Yu, Liang Ding, Xuebo Liu, Kehai Chen, Meishan Zhang, Dacheng Tao, Min Zhang

LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment (2025.emnlp-main)

Challenge:	Experimental results show that PromptST can improve speech-to-text translation by capturing richer linguistic knowledge.
Approach:	They propose a plug-in prompt-enhanced S2T model that captures richer linguistic knowledge . they use a 10GB linguistic probing benchmark to investigate the fusion of speech and text features .
Outcome:	The proposed model can improve on a strong baseline by capturing richer linguistic knowledge.

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Lingyao Li, Dawei Li, Zhenhui Ou, Xiaoran Xu, Jingxiao Liu, Zihui Ma, Runlong Yu, Min Deng

Challenge:	Recent advances in large language models (LLMs) show promise in simulating complex scenarios.
Approach:	They examine multiple LLMs to proactively estimate perceived earthquake impacts using multimodal datasets and multimodal imagery.
Outcome:	The framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales using multimodal datasets.

SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)

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Jun Rao, Yunjie Liao, Xuebo Liu, Zepeng Lin, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

Robustness in NLP

Challenge:	Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning.
Approach:	SeaPO introduces error types commonly occurring in large language models to improve preference learning.
Outcome:	SeaPO introduces error types into model Preference Optimization to improve model performance . negative samples are more erroneous than positive samples, and preference-based training mitigates errors .

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Language Models

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)

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Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li, Qing He

Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)

Challenge:	Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages .
Approach:	They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA.
Outcome:	The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate.

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Xinping Zhao, Jindi Yu, Zhenyu Liu, Jifang Wang, Dongfang Li, Yibin Chen, Baotian Hu, Min Zhang

Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation (2024.acl-long)

Challenge:	Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know".
Approach:	They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content.
Outcome:	The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content.

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Tengfei Yu, Xuebo Liu, Liang Ding, Kehai Chen, Dacheng Tao, Min Zhang

Multilinguality

Challenge:	End-to-end speech translation (ST) models require simultaneous crossmodal and crosslingual transformations to be effective.
Approach:	They propose a homophone-aware contrastive learning approach that integrates a speech-text masking strategy to reduce ambiguity.
Outcome:	The proposed approach achieves SOTA results on BLEU scores on different MuST-C and CoVoST ST tasks, underlining its effectiveness in reducing speech sense ambiguity.

MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)

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Zixiong Yu, Jun Rao, Guhan Chen, Songtao Tian, Bohan Li, Jiansheng Wei, Min Zhang, Xiaojun Meng

Pay More Attention to Images: Numerous Images-Oriented Multimodal Summarization (2025.naacl-long)

Challenge:	Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity.
Approach:	They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task.
Outcome:	The proposed framework outperforms widely-used datasets on eight mathematical benchmarks.

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Min Xiao, Junnan Zhu, Feifei Zhai, Chengqing Zong, Yu Zhou

Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)

Challenge:	Existing multimodal summarization approaches struggle with scenarios involving multiple images as input.
Approach:	They propose a task to generate multimodal summaries by integrating multiple images as input . they propose 'multimodal information evaluation' method that measures differences between generated summary and input based on multimodal input - and compares various methods .
Outcome:	The proposed method correlates more closely with human judgments than five widely used metrics .

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Xiangyu Duan, Hongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

Summarization

Challenge:	Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora.
Approach:	They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task.
Outcome:	The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task.

RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)

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Ziwei Ji, Zihan Liu, Nayeon Lee, Tiezheng Yu, Bryan Wilie, Min Zeng, Pascale Fung

Structured Data in NLP

Challenge:	Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems.
Approach:	They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities .
Outcome:	The proposed approach outperforms state-of-the-art methods on automatic and human evaluation by a large margin.

AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs (2025.emnlp-main)

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Xiaopeng Ke, Hexuan Deng, Xuebo Liu, Jun Rao, Zhenxi Song, Jun Yu, Min Zhang

LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)

Challenge:	Existing approaches to synthesis large language models often suffer from performance limitations and high computational costs.
Approach:	They propose a framework for constructing instruction-tuning data from unlabeled data for any specialized domains from corresponding unlabed data.
Outcome:	The proposed framework is comparable to DeepSeek-V3 while utilizing just 17% of the production cost.

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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)

Challenge:	Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning.
Approach:	They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module.
Outcome:	Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches.

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Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang

Syntactic Text Processing

Challenge:	Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Approach:	They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task.
Outcome:	The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks.

from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors (2025.acl-long)

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Yu Yan, Sheng Sun, Zenghao Duan, Teli Liu, Min Liu, Zhiyi Yin, LeiJingyu LeiJingyu, Qi Li

CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)

Challenge:	Recent studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks.
Approach:	They propose a framework that exploits AdVersArial meTAphoR to induce LLMs to calibrate harmful metaphors for jailbreaking.
Outcome:	The proposed framework can successfully jailbreak Large Language Models (LLMs) by leveraging the AdVersArial meTAphoR (AVATAR) framework achieves state-of-the-art attack success rate across multiple advanced LLMs.

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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song

Speaker-Aware Discourse Parsing on Multi-Party Dialogues (2022.coling-1)

Challenge:	Existing methods for document-level relation extraction (DocRE) lack logic and transparency.
Approach:	They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints.
Outcome:	The proposed framework outperforms existing rule-based frameworks on three DocRE datasets.

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Nan Yu, Guohong Fu, Min Zhang

CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)

Challenge:	Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis.
Approach:	They propose a speaker-aware model for parsing on multi-party dialogues using interaction features between different speakers.
Outcome:	The proposed model achieves the best-reported performance on two standard benchmark datasets.

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Shilin Zhou, Zhenghua Li, Yu Hong, Min Zhang, Zhefeng Wang, Baoxing Huai

InImageTrans: Multimodal LLM-based Text Image Machine Translation (2025.findings-acl)

Challenge:	Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities.
Approach:	They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription.
Outcome:	The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity.

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Fei Zuo, Kehai Chen, Yu Zhang, Zhengshan Xue, Min Zhang

Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)

Challenge:	Existing multimodal large language models suffer from repetition and omission hallucinations when transferred to text image machine translation task.
Approach:	They propose an efficient MLLM named InImageTrans for TiMT and a method for advancing it.
Outcome:	The proposed method outperforms existing open-source MLLMs on the MCiT benchmark.

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Huibin Ruan, Yu Hong, Yang Xu, Zhen Huang, Guodong Zhou, Min Zhang

Challenge:	Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms.
Approach:	They develop a propagative attention learning model using a cross-coupled two-channel network.
Outcome:	The proposed model improves on the baseline models on a Penn Discourse Treebank.

Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)

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Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang

Multilinguality

Challenge:	Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding.
Approach:	They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data .
Outcome:	The proposed method improves translation quality without hurting unconstrained words.

LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)

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Xinxin Li, Huiyao Chen, Chengjun Liu, Jing Li, Meishan Zhang, Jun Yu, Min Zhang

Unsupervised Speech-text word-level alignment with Dynamic Programming (2025.findings-naacl)

Challenge:	Semantic role labeling (SRL) is a crucial task of natural language processing (NLP).
Approach:	They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English.
Outcome:	The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks.

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Tianshu Yu, Zihan Gong, Minghuan Tan, Guhong Chen, Min Yang

The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)

Challenge:	Word-level alignment in speech-text pretraining models is limited by word-level annotated data . authors propose an iterative training method for USDP that reduces the dependency on scarce annotation resources.
Approach:	They propose an Unsupervised Speech-text word-level alignment with Dynamic Programming (USDP) this method uses Dynamic programming principles to iteratively refine temporal alignment predictions .
Outcome:	The proposed method significantly improves on speech-text pretraining tasks compared to existing methods.

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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu

Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)

Challenge:	Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations.
Approach:	They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions.
Outcome:	The proposed framework outperforms white-box methods and reduces computational overhead by over 90%.

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Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)

Challenge:	Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features .
Approach:	They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G.
Outcome:	The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation.

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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang

Programming Languages in NLP

Challenge:	Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns.
Approach:	They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities.
Outcome:	The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages.

Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)

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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

RST Discourse Parsing with Second-Stage EDU-Level Pre-training (2022.acl-long)

Challenge:	Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs.
Approach:	They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information.
Outcome:	The proposed method significantly improves model safety while maintaining utility compared to existing methods.

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Nan Yu, Meishan Zhang, Guohong Fu, Min Zhang

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

Challenge:	Existing pre-trained language models (PLMs) are based on sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).
Approach:	They propose a second-stage EDU-level pre-training approach to learn effective EDU representations continually based on well pre-trained language models.
Outcome:	The proposed method improves F1 score by 2.1 points on a benckmark dataset.

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

Towards Text-Image Interleaved Retrieval (2025.acl-long)

Challenge:	Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing.
Approach:	They build a dataset using DS-generated data as training data and hire annotators to label test data.
Outcome:	The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation.

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Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang

Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)

Challenge:	Existing multimodal information retrieval models rely on single-image inputs . current models use a dense retrieval paradigm, but this approach is not effective .
Approach:	They propose a text-image interleaved retrieval task where query and document are interleaves . they adapt off-the-shelf retrievers and build a dense baseline by interleaded multimodal large language model .
Outcome:	The proposed model achieves significant improvements over the baseline by substantially fewer visual tokens.

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Shilin Zhou, Zhenghua Li, Chen Gong, Lei Zhang, Yu Hong, Min Zhang

Speech & Audio in NLP

Challenge:	Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics.
Approach:	They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions.
Outcome:	The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results.

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)

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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang