Papers by Zhen Yu
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
| Approach: | They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness. |
| Outcome: | The proposed method improves LLMs’ safety over all baselines. |
CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)
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| Challenge: | a number of tools are used to perform complex tasks, but the tool utilization process can cause errors. |
| Approach: | They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks. |
| Outcome: | The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios. |
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026.acl-long)
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Zehua Pei, Hui-Ling Zhen, Lancheng Zou, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
| Challenge: | Large language models (LLMs) are fast but require expensive pre-training . a new approach to scale large language models into MoEs reduces inference costs . |
| Approach: | They propose an analytical post-training framework that rapidly restructures FFNs into sparse MoE architectures using only a small calibration dataset. |
| Outcome: | The proposed framework outperforms existing methods on a small calibration dataset. |
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment (2025.findings-acl)
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| Challenge: | Recent studies have focused on identifying the causes of emotions by understanding verbal contextual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterrances. |
| Approach: | They propose an Emotional Contagion Graph Network that simulates the impact of non-verbal emotional cues on the counterpart’s emotions. |
| Outcome: | The proposed model is compared with state-of-the-art models on a benchmark dataset and the results are encouraging. |
Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)
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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen
| Challenge: | Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped. |
| Approach: | They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation . |
| Outcome: | The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs. |
Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)
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| Challenge: | Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks . |
| Approach: | They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario. |
| Outcome: | The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
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Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention. |
| Approach: | They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments. |
| Outcome: | The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task. |
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification (2020.coling-main)
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| Challenge: | inessential words are unintentionally misjudged as attention-worthy words and assigned heavier attention weights than should be. |
| Approach: | They propose a penalty-based method to regulate the attention learning process by integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. |
| Outcome: | The proposed method improves on the Penn Discourse TreeBank corpus and is competitive compared to the state-of-the-art methods. |
PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)
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| Challenge: | PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements. |
| Approach: | They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance . |
| Outcome: | The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries. |
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Low-rank adaption (LoRA) is a low-level pruning method that can be expensive and slow to deploy. |
| Approach: | They propose a low-rank adaption pruning framework that provides an accurate structured pruned model in a memory-efficient manner. |
| Outcome: | The proposed pruning framework reduces perplexity and memory usage by 52.6% on LLaMA and T5 models while reducing memory usage. |
MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)
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| Challenge: | Text2SQL is a task that translates natural language into SQL statements. |
| Approach: | They propose a task that translates natural language into SQL statements. |
| Outcome: | The proposed task enables users to convert natural language into SQL statements. |
Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale (2026.findings-acl)
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| Challenge: | a large-scale empirical dataset involving 57,954 essays from 10,195 students across 120 schools over two years is presented in this paper. |
| Approach: | They propose a triadic collaboration system that supports K-12 writing learning . they propose linguistic expansion as a pedagogical gatekeeper and bridge . |
| Outcome: | The proposed system improves writing quality through a strategic labor division . authors find that excessive linguistic expansion yields diminishing marginal utility . |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks (2024.naacl-long)
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| Challenge: | Existing black-box attacks require thousands of queries on the target model, making them expensive in real-world applications. |
| Approach: | They propose a new approach that guides word substitutions using prior knowledge from the training set to improve the attack efficiency. |
| Outcome: | The proposed approach reduces query-free attack and guided search attacks by a factor of 10 500 . it improves transferability and generalization by the ensemble of the ABPens in NLP . |
CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)
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Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li
| Challenge: | Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains . |
| Approach: | They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions . |
| Outcome: | The proposed model excels in a teacher-student framework adaptable to evolving domains. |
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats (2026.acl-long)
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| Challenge: | Microscaling Floating-Point (MXFP) is a low-precision format for large language models (LLMs). |
| Approach: | They conduct systematic evaluations of PTQ under Microscaling Floating-Point (MXFP) . they find that MXFP8 consistently achieves near-lossless performance . |
| Outcome: | The proposed method achieves near-lossless performance while MXFP4 introduces substantial accuracy degradation and remains challenging. |
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)
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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao
| Challenge: | Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis. |
| Approach: | They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge. |
| Outcome: | The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents (2026.acl-long)
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| Challenge: | Existing Large language model agents rely on increasingly long interaction histories, resulting in high computational cost and limited scalability. |
| Approach: | They propose a hierarchical reinforcement learning framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories. |
| Outcome: | The proposed framework outperforms baselines on ScienceWorld and ALFWorld benchmarks in terms of performance and generalization while reducing token usage. |
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)
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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions? |
| Approach: | They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods. |
| Outcome: | The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player. |
ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)
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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin
| Challenge: | Mainstream research in natural language processing has focused on high-resource and modern languages. |
| Approach: | They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model . |
| Outcome: | The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)
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| Challenge: | Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments . |
| Approach: | They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts . |
| Outcome: | The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions. |
PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)
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| Challenge: | Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities . |
| Approach: | PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories . |
| Outcome: | The proposed framework achieves higher accuracy and better reliability than current systems. |
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)
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| Challenge: | Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications. |
| Approach: | They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example. |
| Outcome: | The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality. |
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation (2024.findings-naacl)
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| Challenge: | Emotion Recognition in Conversation (ERC) is a task that aims to identify the emotions behind each utterance in a conversation. |
| Approach: | They propose an Emotion-Anchored Contrastive Learning framework that generates more distinguishable utterance representations for similar emotions. |
| Outcome: | The proposed framework achieves state-of-the-art on similar emotions and performs well on similar ones. |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
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| 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. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
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Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |
Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t . |
| Approach: | They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)
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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu
| 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%. |
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)
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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu
| Challenge: | Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands. |
| Approach: | They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans . |
| Outcome: | The proposed approach reduces human intervention and improves safety and efficiency. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |