Papers by Xiaojun Zhang

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

Challenge:	linguistic detection of hyperbole is an important part of understanding human expression . studies on hyperbolic expressions focus on text modality, but social media can be used to detect it .
Approach:	They propose to use a multimodal detection dataset to study hyperbole detection . they treat text and image as two modalities and evaluate pre-trained encoders .
Outcome:	The proposed dataset is constructed from five different keywords and shows its performance.

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

Exploring the Impact of Vision Features in News Image Captioning (2023.findings-acl)

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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Junzhe Zhang, Xiaojun Wan

Are LLM-based Evaluators Confusing NLG Quality Criteria? (2024.acl-long)

Challenge:	Recent state-of-art models can achieve competitive performance even without vision features.
Approach:	They conduct extensive experiments with mainstream news image captioning models to determine whether vision features contribute to the generation of captions.
Outcome:	The proposed models can achieve competitive performance even without vision features.

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Xinyu Hu, Mingqi Gao, Sen Hu, Yang Zhang, Yicheng Chen, Teng Xu, Xiaojun Wan

Text Classification

Challenge:	Existing studies show that LLMs confuse evaluation criteria, which reduces their reliability.
Approach:	They propose a hierarchical classification system for 11 common aspects with corresponding different evaluation criteria.
Outcome:	The proposed system is based on 11 common aspects with different evaluation criteria.

MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency (2025.findings-acl)

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Junzhe Zhang, Huixuan Zhang, Xunjian Yin, Baizhou Huang, Xu Zhang, Xinyu Hu, Xiaojun Wan

SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection (2025.emnlp-main)

Challenge:	Existing benchmarks for knowledge editing in multimodal large language models focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge.
Approach:	They propose a decomposed definition of multimodal knowledge and a benchmark to evaluate it.
Outcome:	The proposed method reveals that it is difficult to define multimodal knowledge editing in LLMs.

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Qin Chen, Yuanyi Ren, Xiaojun Ma, Mugeng Liu, Shi Han, Dongmei Zhang

RST-Guarder: Enhancing Long-Context Robustness for Safeguards via RST Parsing and Probabilistic Inference (2026.acl-long)

Challenge:	Existing automated layout models are ill-suited for spreadsheets, authors say . existing layout models treat components as rectangles with continuous coordinates . authors: spreadsheets are powerful tools for organizing and analyzing data .
Approach:	They formalize a spreadsheet layout generation task and introduce a framework for spreadsheet layouts . they use multimodal large language models to combine rule and vision reflection .
Outcome:	The proposed framework outperforms baselines in a spreadsheet layout generation task by 22.6%.

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Xu Zhang, Xiaojun Wan

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

Challenge:	Existing guardrail models for harmful-content detection degrade on long-form inputs . Existing models are vulnerable to policy-violating responses, causing false positives based on benign content .
Approach:	They propose an inference-time method that improves harmful-content detection for long-form inputs without additional data curation or model training.
Outcome:	The proposed method improves harmful-content detection for long-form inputs without additional data curation or model training.

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Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang

Minos: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
Approach:	They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
Outcome:	The proposed model outperforms existing models by demonstrating its effectiveness and advantages in tool learning.

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Junzhe Zhang, Huixuan Zhang, Xinyu Hu, Li Lin, Mingqi Gao, Shi Qiu, Xiaojun Wan

ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs (2025.acl-long)

Challenge:	Existing evaluation models struggle to achieve consistent performance across image-to-text (I2T) and text-to image (T2I) tasks.
Approach:	They construct a multimodal evaluation model using a large multimodal dataset and rigorous quality control strategies to train it.
Outcome:	The proposed model achieves state-of-the-art evaluation performance across 16 out-of domain datasets covering both I2T and T2I tasks among all open-source multimodal evaluation models and remain competitive with closed-source models.

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Zhenliang Zhang, Xinyu Hu, Huixuan Zhang, Junzhe Zhang, Xiaojun Wan

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods for hallucination detection rely on static and isolated representations, overlooking their dynamic evolution across layers.
Approach:	They propose a method which captures the cross-layer evolution of hidden states and propose 'ICR Probe' which capture the evolution of the hidden states.
Outcome:	The proposed method achieves superior performance with significantly fewer parameters and ablation studies offer deeper insights into the underlying mechanism of the method, improving its interpretability.

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Hanbing Liu, Lang Cao, Yuanyi Ren, Mengyu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han, Dongmei Zhang

JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation (2026.findings-acl)

Challenge:	Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency.
Approach:	They propose a length-aware reward that selectively penalizes insignificance tokens . they also propose 'dynamic length control' that encourages more detailed reasoning .
Outcome:	The proposed method reduces response length while maintaining correctness, the authors show . it selectively penalizes insignificance tokens while maintaining accuracy .

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Fan Xu, Huixuan Zhang, Zhenliang Zhang, Jiahao Wang, Xiaojun Wan

ReasVQA: Advancing VideoQA with Imperfect Reasoning Process (2025.naacl-long)

Challenge:	Existing methods for detecting factual hallucinations in generated content exhibit limitations in the first two stages of the halluciation detection pipeline.
Approach:	They propose a joint claim-and-query generation framework that can detect factual hallucinations in generated content.
Outcome:	The proposed method outperforms existing methods on open-domain QA hallucination detection benchmarks.

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Jianxin Liang, Xiaojun Meng, Huishuai Zhang, Yueqian Wang, Jiansheng Wei, Dongyan Zhao

Visual Data in NLP

Challenge:	Existing approaches to VideoQA often fail when complex reasoning or temporal relationships are involved.
Approach:	They propose a method that leverages reasoning processes generated by Multimodal Large Language Models to improve VideoQA models.
Outcome:	The proposed method improves VideoQA models on three benchmarks.

Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models (2025.findings-emnlp)

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Boyu Jia, Junzhe Zhang, Huixuan Zhang, Xiaojun Wan

MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning (2023.acl-long)

Challenge:	MLLMs have achieved significant breakthroughs in understanding across text and vision, but current models still face inconsistencies in reasoning outcomes.
Approach:	They propose to evaluate multimodal large language models using a multimodal knowledge reasoning dataset to examine the extent of consistency degradation.
Outcome:	The proposed evaluation tasks show that MLLMs are inefficient at integrating knowledge across modalities .

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Xu Zhang, Xiaojun Wan

Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios (2024.naacl-long)

Challenge:	Pre-trained language models generate toxic language which can cause security risks to their applications.
Approach:	They propose a method which detoxifies language models at token-level by interpolating it with a trained multiple instance learning network.
Outcome:	The proposed model outperforms baseline models in detoxification while hurting generation fluency a little bit.

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Xiaojun Kuang, C. L. Philip Chen, Shuzhen Li, Tong Zhang

DecoCal: Decoding with Calibration in Diffusion Large Language Models (2026.acl-long)

Challenge:	Existing methods for few-shot text classification require numerous LMs’ calls to search optimal prompts, thus resulting in overfitting performance and increasing computational cost.
Approach:	They propose a multi-scale knowledge prompt-based memory model that extracts instance-level and class-level knowledge and stores them in memory banks during training.
Outcome:	Experiments on different benchmarks and parameter analysis demonstrate the effectiveness and efficiency of MuSKPrompt in black-box few-shot text classification tasks.

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Fan Xu, Huixuan Zhang, Xiaojun Wan

VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format (2025.findings-emnlp)

Challenge:	Diffusion Large Language Models (DLLMs) generate text via iterative token denoising . but decoding is challenging, with many tokens appearing predictable early .
Approach:	They propose a Decoding framework that performs Calibration of token-level confidence across diffusion steps and leverages the calibrated results to guide decoding decisions.
Outcome:	Experiments on multiple DLLMs and benchmarks show that DecoCal improves generation accuracy compared to existing strategies.

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Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang, Jiansheng Wei, Huishuai Zhang, Dongyan Zhao

Visual Data in NLP

Challenge:	Recent studies on video large language models focus on model architectures and training datasets . interaction format between user and model is unsatisfactory for time-sensitive tasks .
Approach:	They propose a video-text duet interaction format that allows for continuous playback of the video . when a text message ends, the video continues to play, similar to the alternative of two performers in a duet.
Outcome:	The proposed format improves performance on time-sensitive tasks with minimal training efforts.

A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)

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Jingtao Guo, Chunxia Zhang, Lingxi Li, Xiaojun Xue, Zhendong Niu

Semantic Text Processing

Challenge:	Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information.
Approach:	They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system .
Outcome:	The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications.

HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks (2023.findings-acl)

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Zhengkun Zhang, Wenya Guo, Xiaojun Meng, Yasheng Wang, Yadao Wang, Xin Jiang, Qun Liu, Zhenglu Yang

XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)

Challenge:	Pretraining and fine-tuning are the dominant paradigms in natural language processing.
Approach:	They propose a parameter-efficient multitask learning framework that takes trainable hyper-embeddings and visual modality as input and outputs weights for different modules in a pretrained language model.
Outcome:	The proposed framework adds fewer trainable parameters in multi-task learning while achieving superior performances and transfer ability compared to state-of-the-art methods.

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Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models (2026.findings-acl)

Challenge:	Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Approach:	They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Outcome:	The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales.

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Weiwei Qi, Zefeng Wu, Tianhang Zheng, Zikang Zhang, Xiaojun Jia, Zhan Qin, Kui Ren

Metaphor Reasoning is Meta-reasoning (2026.acl-long)

Challenge:	Existing safety-related methodologies for large language models are lacking . despite advances in safety alignment techniques, safeguarding LLMs during adaptation to various tasks remains a challenge.
Approach:	They propose a framework to quantify how different parameters affect LLM safety . they propose two targeted intervention paradigms for safety enhancement and preservation .
Outcome:	The proposed framework reveals safety-critical patterns across different LLM architectures.

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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	Existing work on metaphor reasoning's impact on reasoning abilities is limited.
Approach:	They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable.
Outcome:	The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles.

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

MOVER: Mask, Over-generate and Rank for Hyperbole Generation (2022.naacl-main)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Yunxiang Zhang, Xiaojun Wan

Text Generation

Challenge:	despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing . we use an unsupervised method to generate hyperbolic paraphrases from literal sentences .
Approach:	They propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs.
Outcome:	The proposed method outperforms baseline systems and is based on a large-scale English hyperbole corpus.

PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models (2024.findings-emnlp)

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Huixuan Zhang, Yun Lin, Xiaojun Wan

Semantic Text Processing

Challenge:	Large language models are trained on vast amounts of data, which may unintentionally or intentionally include data from commonly used benchmarks.
Approach:	They propose a set of requirements that practical contamination detection methods should follow to effectively detect benchmark contamination in large language models.
Outcome:	The proposed method detects whether the model is significantly more confident under the original benchmark.

A Survey for Efficient Open Domain Question Answering (2023.acl-long)

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Qin Zhang, Shangsi Chen, Dongkuan Xu, Qingqing Cao, Xiaojun Chen, Trevor Cohn, Meng Fang

Natural Language Interfaces

Challenge:	Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP).
Approach:	They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence.
Outcome:	The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence.

Constrained Tuple Extraction with Interaction-Aware Network (2023.acl-long)

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Xiaojun Xue, Chunxia Zhang, Tianxiang Xu, Zhendong Niu

Information Extraction & Text Mining

Challenge:	Existing knowledge triples lack constraints for their authenticity due to spatial, temporal, or other constraints.
Approach:	They propose a constrained tuple extraction task to guarantee the validity of knowledge tifles by using an interaction-aware network to extract constrained text.
Outcome:	The proposed model outperforms existing models on the dataset and the public CaRB dataset.

Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation (2024.acl-long)

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Xunjian Yin, Xu Zhang, Jie Ruan, Xiaojun Wan

Noise Learning for Text Classification: A Benchmark (2022.coling-1)

Challenge:	Recent advances in large language models have improved performance across tasks . however, the sensitivity of LLMs to prompt leads to unreliability of evaluation results .
Approach:	They propose a new concept to evaluate language models with a fixed question or limited paraphrases as the query.
Outcome:	The proposed method outperforms existing benchmarks on multiple language models . it avoids prompt sensitivity, rendering models more reliable and robust .

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Bo Liu, Wandi Xu, Yuejia Xiang, Xiaojun Wu, Lejian He, Bowen Zhang, Li Zhu

Information Extraction & Text Mining

Challenge:	Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification .
Approach:	They propose to use four state-of-the-art methods of noise learning from the image domain to classify text.
Outcome:	The proposed benchmark of noise learning for text classification is based on four methods and five noise modes.

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

Corpora for Document-Level Neural Machine Translation (2020.lrec-1)

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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Siyou Liu, Xiaojun Zhang

Multilinguality

Challenge:	Document-level machine translation models translate sentences in isolation, but there are three main problems for document-level models.
Approach:	They propose to use document-level machine translation to capture discourse dependencies across sentences by considering a document as a whole.
Outcome:	The proposed method captures discourse dependencies across sentences by considering a document as a whole.

DAMON: A Dialogue-Aware MCTS Framework for Jailbreaking Large Language Models (2025.emnlp-main)

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Xu Zhang, Xunjian Yin, Dinghao Jing, Huixuan Zhang, Xinyu Hu, Xiaojun Wan

WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)

Challenge:	Existing methods for multi-turn attacks mainly utilize a predefined dialogue pattern, limiting their effectiveness in realistic situations.
Approach:	They propose a multi-turn jailbreak attack method that leverages Monte Carlo Tree Search to explore multi-turned conversational spaces and identifies sub-instruction sequences that induce harmful responses.
Outcome:	The proposed method can induce undesired behaviors across five LLMs and three datasets.

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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

Challenge:	despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models.
Approach:	They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese.
Outcome:	The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated .

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu

Natural Language Interfaces

Challenge:	Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks.
Approach:	They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi .
Outcome:	The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level .

MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)

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Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang, Ming Yan, Ji Zhang

R-Bind: Unified Enhancement of Attribute and Relation Binding in Text-to-Image Diffusion Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting.
Approach:	They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions.
Outcome:	The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks.

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Huixuan Zhang, Xiaojun Wan

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)

Challenge:	Existing approaches to improve semantic binding require costly retraining or focus on only correctly generating attributes of entities. Existing methods focus on correctly generating attributes, ignoring the cruciality of correctly forming relations between entities.
Approach:	They propose a training-free method that improves both entity-attribute and entity-relation-entity binding by introducing three inference-time optimization losses that adjust attention maps during generation.
Outcome:	The proposed method improves both entity-attribute and entity-relation-entity binding without additional training.

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Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang

Syntactic Text Processing

Challenge:	Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing.
Approach:	They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character .
Outcome:	The proposed model can learn and benefit from existing tools, but its quality may be poor.

Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization (2025.acl-long)

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Yihao Huang, Chong Wang, Xiaojun Jia, Qing Guo, Felix Juefei-Xu, Jian Zhang, Yang Liu, Geguang Pu