Papers by Ming Xu

Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification (2025.acl-long)

Challenge:	Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach:	They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome:	The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.

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Chengwu Liu, Ye Yuan, Yichun Yin, Yan Xu, Xin Xu, Zaoyu Chen, Yasheng Wang, Lifeng Shang, Qun Liu, Ming Zhang

Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)

Challenge:	Chain-of-Thought prompting is a de facto method to elicit reasoning capabilities from large language models (LLMs).
Approach:	They propose a step-aware formal verification framework Safe to address hallucinations in CoT prompting . they propose 'formal step' as a benchmark for step correctness theorem proving with 30,809 formal statements.
Outcome:	The proposed framework shows significant performance improvement while offering interpretable and verifiable evidence.

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Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Zhilin Gong, Ming Cai, Tianxiang Wang

Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)

Challenge:	Recent advances in natural language processing focus on acquiring lexico-semantic information.
Approach:	They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation.
Outcome:	The proposed model is superior to existing models on a variety of NLU tasks.

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Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, Saloni Potdar

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

Challenge:	Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph.
Approach:	They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once.
Outcome:	The proposed approach can perform state-of-the-art on the benchmark ACE 2005.

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang

Challenge:	GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs.
Approach:	They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training.
Outcome:	The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o .

CoSQA: 20,000+ Web Queries for Code Search and Question Answering (2021.acl-long)

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Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan

Information Retrieval

Challenge:	Using deep neural networks to find codes is difficult . we present a dataset that includes 20,604 labels for natural language queries and codes .
Approach:	They introduce a contrastive learning method to enhance text-code matching . they find that CoSQA improves the accuracy of code question answering by 5.1% .
Outcome:	The proposed method improves the accuracy of code question answering by 5.1% and improves by 10.5% on a CodeBERT model.

SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)

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

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence.
Approach:	They propose a benchmark to evaluate the sociality of role-playing agents using LLMs.
Outcome:	The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances.

RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)

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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma

Challenge:	Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation.
Approach:	They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation.
Outcome:	The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics.

Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)

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Wenzhen Zheng, Wenbo Pan, Xu Xu, Libo Qin, Li Yue, Ming Zhou

MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment (2025.acl-long)

Challenge:	Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive.
Approach:	They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them.
Outcome:	The proposed approach saves significant resources and accelerates convergence and performance.

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Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu

Information Retrieval

Challenge:	Existing personalized product search methods assume that users’ query fully captures their real motivation, but in practice, user's queries do not always articulate the requirements.
Approach:	They propose a Motivation-Aware Personalized Search method that embeds queries and consultations into a unified semantic space via LLMs and utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics.
Outcome:	Extensive experiments on real and synthetic data show that the proposed method outperforms existing methods in retrieval and ranking tasks.

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao

Natural Language Interfaces

Challenge:	Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach:	They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome:	The proposed benchmarks show that the models perform better in open-ended conversations.

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025.acl-long)

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Erxin Yu, Jing Li, Ming Liao, Qi Zhu, Boyang Xue, Minghui Xu, Baojun Wang, Lanqing Hong, Fei Mi, Lifeng Shang

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

Challenge:	Existing approaches to learning from errors synthesize training data by extrapolating from isolated bad cases, thereby failing to generalize the extensive patterns inherent within these cases.
Approach:	They propose a framework that synthesizes more generalized training data from isolated bad cases by extrapolating from isolated cases.
Outcome:	The proposed framework synthesizes more generalized training data to address these model weaknesses.

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding (2025.acl-long)

Challenge:	Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images.
Approach:	They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images.
Outcome:	The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks.

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Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

TheoremQA: A Theorem-driven Question Answering Dataset (2023.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) have improved document understanding performance but generate thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times.
Approach:	They propose a high-resolution document compression module to generate 324 tokens for a single document image.
Outcome:	The proposed module reduces first token latency by more than 50% and improves document comprehension performance.

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Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, Tony Xia

Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

Challenge:	Recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy.
Approach:	They propose to use theorem-driven question-answering dataset to evaluate AI models' ability to apply theoretic concepts to solving challenging science problems.
Outcome:	TheoremQA is curated by domain experts and contains 800 high-quality questions covering 350 theoremics from Math, Physics, EE&CS, and Finance.

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Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun

Natural Language Interfaces

Challenge:	Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach:	They propose a dataset that allows users to ask clarification questions using open-domain examples.
Outcome:	The proposed model achieves better performance than strong baselines and provides new challenges.

E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning (2021.acl-long)

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Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang

Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)

Challenge:	Existing vision-language pre-training methods use a two-step training procedure to learn visual features from image-text pairs.
Approach:	They propose a vision-language pre-trained model for V+L understanding and generation using a unified Transformer framework.
Outcome:	The proposed model can learn visual representation and semantic alignments between image and text on visual-text pairs and on visual processing tasks.

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Wenjie Zhou, Qiang Wang, Mingzhou Xu, Ming Chen, Xiangyu Duan

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)

Challenge:	Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models.
Approach:	They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format .
Outcome:	The proposed questions are shuffle, label replacement, and True/False format.

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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)

Challenge:	Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks.
Approach:	They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation.
Outcome:	EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% .

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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu

Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model.
Approach:	They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures.
Outcome:	The proposed framework yields significant performance gains on Twitter and other platforms.

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Bo Xu, Shufei Li, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

Programming Languages in NLP

Challenge:	Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries .
Approach:	They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors.
Outcome:	The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.

FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (2022.findings-emnlp)

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Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, Ming Cai

Syntactic Text Processing

Challenge:	grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers.
Approach:	They propose a human-annotated corpus to detect, identify and correct grammatical errors in Chinese examinations.
Outcome:	The proposed model outperforms other models in low-resource settings, but there is a significant gap between the models and humans that encourages future models to bridge it.

SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)

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Wenru Xu, Peixuan Xu, Ziqi Yang, Ming Hu, Zihui Wang, Jianzhong Qi, Rongshan Yu, Xiaoliang Fan, Cheng Wang

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)

Challenge:	Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification.
Approach:	They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification.
Outcome:	The proposed framework accelerates inference while reducing the LLM usage costs.

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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency.
Approach:	They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment .
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

Retrieval Enhanced Model for Commonsense Generation (2021.findings-acl)

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

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Han Wang, Yang Liu, Chenguang Zhu, Linjun Shou, Ming Gong, Yichong Xu, Michael Zeng

Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)

Challenge:	Existing frameworks for commonsense generation are lacking for pre-trained models.
Approach:	They propose a framework that uses concept matching to retrieve prototype sentences and trainable sentence retriever to enhance pre-training and fine-tuning.
Outcome:	The proposed framework achieves state-of-the-art on the large-scale Common-Gen benchmark.

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

Challenge:	Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions.
Approach:	They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics.
Outcome:	The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively.

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Kuan Xu, Baoxin Zhang, Shuyue Fan, Ming Chen, Zhipeng Ke, Jian Yu, Xuezhong Zhou

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for Zero-shot Relational Learning depend on external knowledge, resulting in increased annotation costs and limited practical applicability.
Approach:	They propose a structure-aware paradigm that performs ZRL without external knowledge . it leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones.
Outcome:	The proposed paradigm achieves 10.66% improvement in MRR while reducing annotation costs and enhancing practical applicability on three real-world benchmarks.

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling.
Approach:	They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space.
Outcome:	The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Recall and Learn: A Memory-augmented Solver for Math Word Problems (2021.findings-emnlp)

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

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Shifeng Huang, Jiawei Wang, Jiao Xu, Da Cao, Ming Yang

Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)

Challenge:	Existing methods for solving math word problems are based on template-based generation which results in limited generalization capability.
Approach:	They propose a human-like analogical learning method for the math word problem . it uses modules of memory, representation, analogy, and reasoning to make a new exercise .
Outcome:	The proposed method outperforms state-of-the-art models on two well-known datasets.

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Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong

TinyChart: Efficient Chart Understanding with Program-of-Thoughts Learning and Visual Token Merging (2024.emnlp-main)

Challenge:	Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs.
Approach:	They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them.
Outcome:	Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks.

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Liang Zhang, Anwen Hu, Haiyang Xu, Ming Yan, Yichen Xu, Qin Jin, Ji Zhang, Fei Huang

Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)

Challenge:	Recent studies have shown that multimodal large language models can be useful for chart understanding, but their size limits their use in resource-constrained environments.
Approach:	They propose an efficient multimodal large language model with only 3B parameters for chart understanding.
Outcome:	The proposed model outperforms several chart-understanding MLLMs with up to 13B parameters on ChartQA, Chart-to-Text, Chart to Table, OpenCQA, and ChartX.

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Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang

DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)

Challenge:	Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach:	They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome:	The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou

Challenge:	Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information.
Approach:	They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation .
Outcome:	The proposed model trains on DocBank accurately recognize layout information for a variety of documents.

AgentOCR: Reimagining Agent History via Optical Self-Compression (2026.acl-long)

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Lang Feng, Fuchao Yang, Feng Chen, Xin Cheng, Haiyang Xu, Zhenglin Wan, Ming Yan, Bo An

Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have enabled agentic systems trained with reinforcement learning over multi-turn interaction, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token and memory costs.
Approach:	They propose a framework that represents the accumulated observation-action history as a compact rendered image.
Outcome:	The proposed framework preserves over 95% of text-based agent performance while significantly reducing token consumption (>50%), yielding consistent token and memory efficiency.

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Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou

Machine Translation

Challenge:	Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available.
Approach:	They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities .
Outcome:	The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text.

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou

BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach:	They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
Outcome:	The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design .

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Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou

Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)

Challenge:	a novel approach to compress neural networks by progressive module replacement is proposed . a number of techniques have been proposed to compress pretraining and fine-tuning models .
Approach:	They propose a model compression approach that divides BERT into modules and builds their compact substitutes.
Outcome:	The proposed approach outperforms existing knowledge distillation approaches on GLUE benchmark . it is based on a model that divides the original BERT into several modules and builds their substitutes .

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Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei, Ming Zhou

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents.
Approach:	They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents.
Outcome:	The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories (2024.lrec-main)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Hongzhi Xu, Jingxia Lin, Sameer Pradhan, Mitchell Marcus, Ming Liu

Challenge:	a recent study has shown that large language models can be useful for cross-lingual applications.
Approach:	They propose to annotate Chinese word senses using English WordNet synsets . they examine the relationship between two annotators and find patterns among synset .
Outcome:	The proposed method shows that the annotators agree on 38% of the synsets compared with the original synset . the results highlight similarities between the synnotated synset and the WordNet structure .

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)

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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Structured Data in NLP

Challenge:	Existing methods for QA in industrial environments are inherently relational and often updated.
Approach:	They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning.
Outcome:	Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity.

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)

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Hongxiao Zhang, Mingtong Liu, Chunyou Li, Yufeng Chen, Jinan Xu, Ming Zhou

Text Generation

Challenge:	Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge .
Approach:	They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data.
Outcome:	The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge.

ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)

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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts.
Approach:	They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts.
Outcome:	The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average.

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

Syntax-Enhanced Pre-trained Model (2021.acl-long)

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan

Syntactic Text Processing

Challenge:	Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages.
Approach:	They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages.
Outcome:	The proposed model achieves state-of-the-art on six public benchmark datasets.

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)

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Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu, Ming Zhang, Sheng Wang

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Existing meta-path generation methods cannot fully exploit rich textual information in HINs.
Approach:	They propose a text-infilling-based approach to generate meta-paths from textual information in HINs.
Outcome:	The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

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Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Structured Data in NLP

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)

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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning.
Approach:	They propose a framework that introduces path-centric reward shaping for agentic RAG training.
Outcome:	The proposed framework improves on existing methods with an average accuracy gain of 7.7 points.

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Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong

Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (2021.emnlp-main)

Challenge:	Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions.
Approach:	They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts.
Outcome:	The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios.

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Yingmei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang

Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)

Challenge:	Existing approaches to scale out spoken language understanding to low-resource languages are noisy.
Approach:	They propose a method for mitigating noise in augmented data by training models with augmented datasets.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Bo Xu, Haiqi Jiang, Jie Wei, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

Scheduled DropHead: A Regularization Method for Transformer Models (2020.findings-emnlp)

Challenge:	Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context.
Approach:	They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image .
Outcome:	The proposed method can be integrated into existing models and demonstrate consistent performance improvements.

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Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou, Ke Xu

Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)

Challenge:	DropHead is a structured dropout method for regularizing multi-head attention . DropHed drops entire attention heads during training to prevent overfitting .
Approach:	They propose a structured dropout method specifically designed for regularizing multi-head attention mechanism . DropHead drops entire attention heads during training to prevent overfitting .
Outcome:	The proposed method can improve transformer models by 0.9 BLEU score on translation task and around 1.0 accuracy for various text classification tasks.

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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang

MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)

Challenge:	Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods.
Approach:	They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers.
Outcome:	The proposed framework can be used to prove hard mode statements on ATP benchmarks.

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Chunyou Li, Mingtong Liu, Hongxiao Zhang, Yufeng Chen, Jinan Xu, Ming Zhou

Low-Resource NLP

Challenge:	Sentence-level translation, document-level and terminology constrained translations are important in machine translation.
Approach:	They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning .
Outcome:	The proposed model improves translation memory, document-level translation, and document-constrained translation tasks.

CxGGEC: Construction-Guided Grammatical Error Correction (2025.acl-long)

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Yayu Cao, Tianxiang Wang, Lvxiaowei Xu, Zhenyao Wang, Ming Cai

Syntactic Text Processing

Challenge:	Current GEC methods rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language.
Approach:	They propose to use construction grammar to capture underlying language patterns and guide corrections by decoding construction tokens into their original forms and correcting erroneous tokens.
Outcome:	The proposed model captures underlying language patterns and corrects erroneous construction tokens on English and Chinese benchmarks.

Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)

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Bo Xu, Yifei Wu, Shouang Wei, Ming Du, Hongya Wang

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)

Challenge:	Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation.
Approach:	They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback.
Outcome:	The proposed method achieves state-of-the-art on seven semantic text similarity tasks.

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Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou

Linguistics & Cognitive NLP

Challenge:	Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured.
Approach:	They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory.
Outcome:	The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

CoELM: Construction-Enhanced Language Modeling (2024.acl-long)

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

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Lvxiaowei Xu, Zhilin Gong, Jianhua Dai, Tianxiang Wang, Ming Cai, Jiawei Peng

Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math (2026.acl-long)

Challenge:	Recent studies show that integrating constructional information can improve the performance of pre-trained language models.
Approach:	They propose a construction-Enhanced language model that embeds constructional semantics into language models for natural language generation.
Outcome:	The proposed model outperforms existing models on various benchmarks.

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Shrey Pandit, Austin Xu, Xuan-Phi Nguyen, Yifei Ming, Caiming Xiong, Shafiq Joty

ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)

Challenge:	Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition .
Approach:	They propose a human-annotated step-level verification benchmark that measures step- level verifiers at the frontier.
Outcome:	The proposed benchmark outperforms closed-source models in step-level verification and the impact of scaling verifier compute.

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Xingjian Diao, Weiyi Wu, Keyi Kong, Peijun Qing, Xinwen Xu, Ming Cheng, Soroush Vosoughi, Jiang Gui

Question Answering

Challenge:	Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify.
Approach:	They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant.
Outcome:	The proposed framework yields faithful, fine-grained explanations while maintaining competitive accuracy.

Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)

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Bo Xu, Shizhou Huang, Ming Du, Hongya Wang, Hui Song, Chaofeng Sha, Yanghua Xiao

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	Recent multimodal information extraction approaches overestimate the significance of images.
Approach:	They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities.
Outcome:	The proposed method outperforms existing models on two different multimodal information extraction tasks.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

Explainability & Interpretability in NLP

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)

Challenge:	Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks.
Approach:	They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance.
Outcome:	The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks.

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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han

When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs (2026.findings-acl)

Challenge:	Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines.
Approach:	They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster.
Outcome:	The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines.

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Zhongxiang Sun, Yi Zhan, Chenglei Shen, Weijie Yu, Xiao Zhang, Ming He, Jun Xu

Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu

Challenge:	Personalization can inadvertently distort factual reasoning when faced with factual queries.
Approach:	They propose a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior.
Outcome:	Experiments across multiple LLM backbones and personalization methods show that FPPS significantly improves factual accuracy while maintaining personalized performance.

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Information Retrieval

Challenge:	Existing methods rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but this lacks exploration of needs in user consultations.
Approach:	They propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value.
Outcome:	The proposed model outperforms baselines on public and commercial datasets on both retrieval and ranking tasks.

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)

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Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang

Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings (2025.acl-long)

Challenge:	Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models .
Approach:	They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference.
Outcome:	The proposed model can speed up training and inference by 40% over previous models.

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Austin Xu, Srijan Bansal, Yifei Ming, Semih Yavuz, Shafiq Joty

Challenge:	Contextual evaluation is challenging for state-of-the-art judge models . evaluation criteria are often conditional and dependent on practitioner priorities .
Approach:	They propose a judge benchmark that evaluates large language models as judges in contexts . they use human annotations and model-based perturbations to build the benchmark .
Outcome:	The proposed benchmark aims to evaluate large language models in contexts with 2,000 challenging response pairs.

CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution (2025.acl-long)

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Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun

Programming Languages in NLP

Challenge:	Existing code benchmarks focus on code generation, while those for code reasoning are insufficient.
Approach:	They propose a multi-lingual code reasoning benchmark that contains 19 programming languages and at least 600 subjects for each language.
Outcome:	The proposed model trains on Python and achieves 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.

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

Profiling LLM’s Copyright Infringement Risks under Adversarial Persuasive Prompting (2025.findings-emnlp)

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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Jikai Long, Ming Liu, Xiusi Chen, Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang

CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)

Challenge:	Large Language Models have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement.
Approach:	They propose a structured persuasion workflow to analyze the influence of persuasive prompts on LLM outputs.
Outcome:	The proposed method analyzes the influence of persuasive prompts on LLM outputs.

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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin

BERT-based Lexical Substitution (P19-1)

Challenge:	Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up.
Approach:	They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning.
Outcome:	The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets.

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Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, Ming Zhou

Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)

Challenge:	Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence.
Approach:	They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources.
Outcome:	The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.

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Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)

Challenge:	Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions.
Approach:	They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph.
Outcome:	The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm.

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Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, Jianxin Liao, Tong Xu, Ming Liu

Sentiment Analysis

Challenge:	Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features.
Approach:	They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context.
Outcome:	The proposed model achieves state-of-the-art on three datasets.

FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)

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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li