Papers by Xiaoming Zhang
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graph models are inefficient at capturing complex temporal dynamics and hierarchical relations within TKGs. |
| Approach: | They propose to use hyperbolic geometry to effectively model temporal knowledge graphs . they use the hyperbolical gated Graph Neural Network and the hyperbipolar convolutional neural network . |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmark datasets . it is compared with previous models and is expected to be useful in real-world applications . |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training (2025.acl-long)
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| Challenge: | Existing MGT detectors are vulnerable to simple perturbations and adversarial attacks. |
| Approach: | They propose an adversarial framework for training a robust machine-generated text detector called GREedy Adversary PromoTed DefendER. |
| Outcome: | The proposed framework reduces the Attack Success Rate (ASR) by 0.67% compared with SOTA defense methods. |
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)
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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang
| Challenge: | Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications. |
| Approach: | They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics . |
| Outcome: | The proposed model performs best on KwaiChat but is not perfect in this situation. |
Confidence Should Be Calibrated More Than One Turn Deep (2026.acl-long)
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| Challenge: | Existing work on confidence estimation and calibration focuses on single-turn settings . existing work on multi-turn calibration ignores the risks and potential of multi-turned conversations . |
| Approach: | They propose a multi-turn calibration task that reframes calibration from a static property into a dynamic challenge central to reliable multi- turn conversations. |
| Outcome: | The proposed model minimizes ECE@T and leverages ConfChat to improve confidence . the proposed model preserves and even enhances model performance in multi-turn interactions. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen
| Challenge: | Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models. |
| Approach: | They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model. |
| Outcome: | The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD. |
Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing (2026.acl-long)
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Yirui QI, Xiaoming Zhang, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan, Zhenyu Guan
| Challenge: | Existing LLMs conflate identity consistency with emotional rigidity . Existing models exhibit either robotic repetition or persona drift . |
| Approach: | They propose a framework that decouples Identity-Layer Stability from Adaptive-Layer Appropriateness to achieve persona coherence repair. |
| Outcome: | Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 show consistent improvements (+16–84% gains) |
Hierarchy Response Learning for Neural Conversation Generation (D19-1)
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| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
| Approach: | They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model . |
| Outcome: | The proposed model can generate the responses with more appropriate content and expression. |
Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)
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| Challenge: | Existing claim detection benchmarks treat claims as static textual artifacts . current research ignores sociological etiology of how information naturally emerges and mutates . |
| Approach: | They propose a socially generative framework for synthetic claim generation . they propose utterance, proposition and context-based simulations to capture truth decay . |
| Outcome: | The proposed paradigm models claims as socially evolving entities . it allows precise simulation of truth decay and intervened propagation with multi-auditor oversight . |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
Integrating Representation Subspace Mapping with Unimodal Auxiliary Loss for Attention-based Multimodal Emotion Recognition (2024.lrec-main)
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Xulong Du, Xingnan Zhang, Dandan Wang, Yingying Xu, Zhiyuan Wu, Shiqing Zhang, Xiaoming Zhao, Jun Yu, Liangliang Lou
| Challenge: | Existing methods to identify emotions rely on a large modality gap in their representations . |
| Approach: | They propose a representation subspace mapping module that maps each modality into two distinct subspaces and a cross-modality attention module that leverages auxiliary loss to remove the noise unrelated to emotion classification. |
| Outcome: | The proposed approach achieves superior performance to state-of-the-art MER methods on the IEMOCAP and MSP-Improv datasets. |
Keyphrase Generation with Correlation Constraints (D18-1)
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| Challenge: | Existing methods for keyphrase generation ignore correlation among keyphrases, resulting in duplication and coverage issues. |
| Approach: | They propose a new sequence-to-sequence architecture for keyphrase generation that captures correlation among keyphrases by preceding phrases to eliminate duplicate phrases and improve result coherence. |
| Outcome: | The proposed model outperforms the state-of-the-art method on benchmark datasets in terms of accuracy and diversity. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang
| Challenge: | Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients. |
| Approach: | They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems. |
| Outcome: | The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective. |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
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| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data (2026.acl-long)
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| Challenge: | Existing benchmarks do not assess agents’ capabilities across data types . Existing tools only evaluate agents' ability to extract reasonable insights across data formats. |
| Approach: | They propose a multi-source benchmark to evaluate the performance of data analytics agents in handling diverse data sources. |
| Outcome: | The proposed agent performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS (2026.acl-long)
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| Challenge: | Existing tool attacks are limited by domain specificity or fixed and static templates. |
| Approach: | They propose an attack-based memory-augmented reinforcement learning process that constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns. |
| Outcome: | Evo-Attacker outperforms baselines in the long-horizon credit assignment challenge. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
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| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
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| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)
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| Challenge: | Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error. |
| Approach: | They propose to use flowcharts to evaluate existing LLMs' code generation capabilities. |
| Outcome: | The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance. |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
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| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)
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| Challenge: | Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models. |
| Approach: | They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness. |
| Outcome: | The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model. |
StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)
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| Challenge: | Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting. |
| Approach: | They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization. |
| Outcome: | Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average. |
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)
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| Challenge: | Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity. |
| Approach: | They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics. |
| Outcome: | The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios. |