Papers by Chong Zhang

We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)

Challenge:	Traditional video topic segmentation methods struggle to discern topical transitions . supervised approaches have improved performance on video action or scene segmentation .
Approach:	They propose a new task for video topic segmentation that enhances multimodality alignment and fusion by exploring different architectures using Cross-Attention and Mixture of Experts.
Outcome:	The proposed model improves on educational videos, in the form of lectures . it combines cross-attention and mixture of experts to strengthen multimodality alignment and fusion .

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang

RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories (2026.findings-acl)

Challenge:	Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach:	They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome:	The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.

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Yanlin Wang, Ziyao Zhang, Chong Wang, Xinyi Xu, Mingwei Liu, Yong Wang, Jiachi Chen, Zibin Zheng

Programming Languages in NLP

Challenge:	Existing benchmarks for large language models fail to capture complex interplay between functionality and security.
Approach:	They propose a benchmark for secure code generation constructed from real-world, high-risk Java repositories.
Outcome:	The proposed benchmarks highlight the gap between functional and secure code generation in LLMs.

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling (2023.emnlp-main)

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Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu, Qian Chen, Wen Wang

Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)

Challenge:	Recent supervised neural models have greatly promoted the development of topic segmentation, but the deeper relationship between coherence and topic segmenting is underexplored.
Approach:	They propose to use topic-aware Sentence Structure Prediction and Contrastive Semantic Similarity Learning to capture coherence from logical structure and semantic similarity perspectives to further improve topic segmentation performance.
Outcome:	The proposed approach outperforms state-of-the-art methods on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection.

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Donghang Duan, Xu Zheng, Yuefeng He, Chong Mu, Leyi Cai, Lizong Zhang

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

Challenge:	Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse.
Approach:	They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks.
Outcome:	The proposed framework achieves superior privacy-utility trade-off compared to strong baselines.

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Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, ShiLiang Zhang, Bing Li, Wei Wang, Xin Cao

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)

Challenge:	Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach:	They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome:	The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .

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Li Zheng, Xin Zhang, Shuyi He, Fei Li, Chong Teng, Jiang-Ming Yang, Donghong Ji, Zhuang Li

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Challenge:	Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections.
Approach:	They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening .
Outcome:	The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons.

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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

Information Retrieval

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models (2024.findings-emnlp)

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Chen Zhang, Chengguang Tang, Dading Chong, Ke Shi, Guohua Tang, Feng Jiang, Haizhou Li

Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models (2024.emnlp-main)

Challenge:	Mainstream approaches to aligning large language models heavily rely on human preference data.
Approach:	They propose a framework that fine-tunes a policy model using pairwise feedback data automatically mined from its outputs.
Outcome:	The proposed framework outperforms the base model with an average win rate of 69.7% across seven conversational or instruction-following datasets.

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Keqin Bao, Jizhi Zhang, Yang Zhang, Xinyue Huo, Chong Chen, Fuli Feng

X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)

Challenge:	Existing approaches to adapt Large Language Models (LLMs) for recommendation encounter significant challenges such as amplification bias and homogeneity.
Approach:	They propose a new decoding approach called Debiasing-Diversifying Decoding (D3) that disables length normalization for ghost tokens to alleviate amplification bias and incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity.
Outcome:	Extensive experiments on real-world datasets demonstrate the proposed approach’s effectiveness in enhancing accuracy and diversity.

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Chong Li, Wen Yang, Jiajun Zhang, Jinliang Lu, Shaonan Wang, Chengqing Zong

Multilinguality

Challenge:	Large language models respond well in high-resource languages but struggle in low-resourced languages.
Approach:	They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages.
Outcome:	The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages.

DEMO: A Statistical Perspective for Efficient Image-Text Matching (2024.naacl-long)

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Fan Zhang, Xian-Sheng Hua, Chong Chen, Xiao Luo

Visual Data in NLP

Challenge:	Image-text matching is a problem that seeks to connect vision and language through semantic understanding.
Approach:	They propose a deep unsupervised hashing-based approach for image-text matching . they characterize each image using multiple augmented views, which are considered as samples .
Outcome:	The proposed approach achieves superior performance on image-text matching datasets compared with state-of-the-art methods.

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.

Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness (2025.findings-emnlp)

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Lingnan Xu, Chong Feng, Kaiyuan Zhang, Liu Zhengyong, Wenqiang Xu, Fanqing Meng

Information Retrieval

Challenge:	Existing approaches to retrieval-augmented generation ignore valuable structure that is crucial for document organization.
Approach:	They propose a framework that explicitly incorporates structural information throughout the RAG process.
Outcome:	The proposed framework incorporates structural information throughout the RAG process.

TokAlign: Efficient Vocabulary Adaptation via Token Alignment (2025.acl-long)

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Chong Li, Jiajun Zhang, Chengqing Zong

Green & Sustainable NLP

Challenge:	Tokenization is a foundational step for Large Language Models (LLMs) but low compression rate of vanilla tokenizers decelerates training and inference process.
Approach:	They propose a method to replace the vocabulary of Large Language Models (LLMs) by learning a one-to-one mapping matrix for token IDs.
Outcome:	The proposed method significantly improves multilingual text compression rates and vocabulary initialization for Large Language Models.

Pardon? Evaluating Conversational Repair in Large Audio-Language Models (2026.findings-acl)

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Shuanghong Huang, Jinlei Xu, Youchao Zhou, Yanghao Zhou, Xuan Zhao, Chong Feng, Wenxuan Zhang

Natural Language Interfaces

Challenge:	Existing evaluations of large audio-language models focus on answer accuracy and robustness to acoustic perturbations, but they assume that inputs remain semantically answerable.
Approach:	They propose a repair-aware evaluation setting that explicitly distinguishes between answerable and unanswerable audio inputs.
Outcome:	The proposed evaluation setting distinguishes between answerable and unanswerable audio inputs.

Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)

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Chong Zhang, Ya Guo, Yi Tu, Huan Chen, Jinyang Tang, Huijia Zhu, Qi Zhang, Tao Gui

Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025.findings-emnlp)

Challenge:	Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs).
Approach:	They propose a method to predict token sequences within visually-rich documents by a simple prediction head.
Outcome:	The proposed method can be used to predict token mentions as token sequences within documents.

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Yunhao Zhang, Shaonan Wang, Nan Lin, Xinyi Dong, Chong Li, Chengqing Zong

Explainability & Interpretability in NLP

Challenge:	Existing approaches focus on predefined dimensions that overlook finer conceptual distinctions . a new framework is proposed to investigate the subdimensions underlying coarse-grained semantic dimensions .
Approach:	They propose a framework that decomposes word embeddings into multiple sub-embeddings . they propose to map these subdimensions to brain activation to assess their plausibility .
Outcome:	The proposed framework decomposes word embeddings from large language models into sub-embeddings, each encoding specific semantic information.

Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation (2021.emnlp-main)

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Tianfu Zhang, Heyan Huang, Chong Feng, Longbing Cao

Machine Translation

Challenge:	Existing methods to improve multi-head self-attention are lacking in many languages.
Approach:	They propose a redundant head enlivening method to identify redundant heads and vitalize their potential by learning syntactic relations and prior knowledge in the text.
Outcome:	The proposed method can identify and vitalize redundant heads without sacrificing the roles of important heads.

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)

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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li

Challenge:	Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives.
Approach:	They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.
Outcome:	The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.

PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)

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Yue Zhang, Zhihao Zhang, Wenbin Lai, Chong Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing.
Approach:	They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure.
Outcome:	The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%.

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model (2025.findings-acl)

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Chong Li, Yingzhuo Deng, Jiajun Zhang, Chengqing Zong

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

Challenge:	Experimental results show that language group specialization on experts improves multilingual performance.
Approach:	They propose to dynamically group and scale up parameters of multilingual Large Language Models while boosting positive transfer among similar languages.
Outcome:	The proposed method reduces negative transfer between languages and boosts performance on 18 to 128 languages.

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

Reasoning

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

A Partition Filter Network for Joint Entity and Relation Extraction (2021.emnlp-main)

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Zhiheng Yan, Chong Zhang, Jinlan Fu, Qi Zhang, Zhongyu Wei

Information Extraction & Text Mining

Challenge:	Existing approaches to extract entity and relation feature are flawed because they do not consider the intimate connection between NER and RE.
Approach:	They propose a partition filter network to model two-way interaction between tasks . they leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Outcome:	The proposed model performs significantly better than previous approaches on six public datasets.

Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)

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Chong Li, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC)
Approach:	They propose a sequence-to-sequence learning framework with neural networks that generates more valuable training instances and adds task-specific examples to enhance the model.
Outcome:	The proposed method improves generalization and robustness of multiple CSC models across three datasets.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

Structured Data in NLP

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)

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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang

Dialogue Systems & Conversational Agents

Challenge:	Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge .
Approach:	They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM.
Outcome:	The proposed model can model human conversation behaviors with low latency and natural interactions with low delay.

Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow (2025.findings-emnlp)

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Jianfei Zhao, Feng Zhang, Xin Sun, Chong Feng

Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)

Challenge:	Decode-Only models propagate information from left to right, but the model's attention still focuses on the visual representations, resulting in hallucinations.
Approach:	They propose to leverage the core information embedded in semantic representations to enhance the model's visual understanding by leveraging the attention distributions.
Outcome:	The proposed method reduces hallucinations by 80% by aligning the attention distribution with the actual information flow.

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Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu

Information Extraction & Text Mining

Challenge:	Text classification is a primary task in natural language processing (NLP).
Approach:	They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification.
Outcome:	The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.

Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)

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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang

TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)

Challenge:	Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments.
Approach:	They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization.
Outcome:	The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings .

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Xiaorui Wu, Xiaofeng Mao, Fei Li, Xin Zhang, Xuanhong Li, Chong Teng, Donghong Ji, Zhuang Li

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning (2026.findings-acl)

Challenge:	Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes.
Approach:	They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics.
Outcome:	The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics.

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Jingbo Sun, Wenyue Chong, Songjun Tu, Qichao Zhang, Yaocheng Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, Dongbin Zhao

Green & Sustainable NLP

Challenge:	Prior work limits search depth to reduce cost, but this often leads to underexploration of complex questions.
Approach:	They propose a reinforcement learning framework that evaluates each search step via self-generated intermediate answers.
Outcome:	Extensive experiments on multiple benchmarks show that AutoSearch achieves a superior accuracy-efficiency trade-off, alleviating over-searching while preserving search quality.

Enhancing Recommendation Explanations through User-Centric Refinement (2025.findings-emnlp)

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Jingsen Zhang, Zihang Tian, Xueyang Feng, Xu Chen, Chong Chen

Explainability & Interpretability in NLP

Challenge:	Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users.
Approach:	They propose a paradigm that refines initial explanations generated by existing models during the inference stage to enhance their quality in multiple aspects.
Outcome:	The proposed model improves explanations generated by existing models during the inference stage to enhance their quality in multiple aspects.

Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021.naacl-main)

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Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh

Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning (2023.emnlp-main)

Challenge:	Recent studies show that NLP models are vulnerable to adversarial perturbations such as synonym substitutions or syntax-guided paraphrasing.
Approach:	They propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset.
Outcome:	The proposed attack achieves high success rates on both original and robustly trained CNNs and Transformers.

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Chong Li, Shaonan Wang, Yunhao Zhang, Jiajun Zhang, Chengqing Zong

Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)

Challenge:	Experimental results show that multi-head attention module evolves functional specialization after multi-task training.
Approach:	They propose a method to quantify the degree of functional specialization in multi-head attention . they propose 'multi-task training' method to increase functional specialisation and mitigate negative information transfer .
Outcome:	The proposed method increases functional specialization and mitigates negative information transfer in multi-task learning without adding any parameters.

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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang

Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment (2024.naacl-long)

Challenge:	Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning.
Approach:	They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings.
Outcome:	Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks.

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Chong Li, Shaonan Wang, Jiajun Zhang, Chengqing Zong

Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)

Challenge:	Existing studies show that multilingual generative models exhibit a strong language bias toward high-resource languages.
Approach:	They propose a cross-lingual alignment framework exploiting pairs of translation sentences to improve cross-linguistic abilities.
Outcome:	The proposed framework improves cross-lingual abilities and mitigates performance gap.

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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang

Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)

Challenge:	PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations .
Approach:	They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations .
Outcome:	The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD.

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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui

Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)

Challenge:	Existing models of layout reading order do not convey the complete reading order information in the layout.
Approach:	They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance .
Outcome:	The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization.

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Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao

Reasoning

Challenge:	Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training.
Approach:	They propose a lazy length penalty that imposes length pressure on models without extra training stages.
Outcome:	The proposed method significantly reduces response length without extra training stages while maintaining or improving performance.

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025.acl-long)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng

Green & Sustainable NLP

Challenge:	Long-context modeling is crucial for next-generation language models, but high computational cost of standard attention mechanisms poses significant computational challenges.
Approach:	They propose a natively trained Sparse Attention mechanism that integrates algorithms with hardware-aligned optimizations to achieve efficient long-context modeling.
Outcome:	The proposed model maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning.

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

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Chunyu Ye, Yunhao Zhang, Jingyuan Sun, Chong Li, Yang Zhao, Shaonan Wang

Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training (2021.emnlp-main)

Challenge:	Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach:	They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome:	The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.

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Zhengyan Li, Yicheng Zou, Chong Zhang, Qi Zhang, Zhongyu Wei

Sentiment Analysis

Challenge:	Recent studies have focused on identifying the sentiment polarity of aspects in product reviews.
Approach:	They propose to use supervised Contrastive Pre-Training to learn implicit sentiment . they propose to train large-scale sentiment-annotated corpora from in-domain language resources .
Outcome:	The proposed model achieves state-of-the-art performance on SemEval2014 benchmarks and comprehensively validates its effectiveness on learning implicit sentiment.

Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)

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Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji

Calibrating the Confidence of Large Language Models by Eliciting Fidelity (2024.emnlp-main)

Challenge:	Existing methods for multimodal sentiment analysis are often dynamically incomplete.
Approach:	They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment.
Outcome:	The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models.

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Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu

MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models (2025.emnlp-main)

Challenge:	Large language models with RLHF and RLAIF have good alignment but exhibit overconfidence post-alignment.
Approach:	They propose a plug-and-play method to estimate the confidence of large language models.
Outcome:	The proposed method has shown good calibration performance on 6 RLHF-LMs on four MCQA datasets.

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Siyu Yan, Long Zeng, Xuecheng Wu, Chengcheng Han, Kongcheng Zhang, Chong Peng, Xuezhi Cao, Xunliang Cai, Chenjuan Guo

Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)

Challenge:	Existing defenses target single-turn attacks, but real-world usage involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures.
Approach:	They propose a framework that tackles multi-turn jailbreaks from both attack and defense angles.
Outcome:	Experiments on large language models show that MUSE effectively mitigates multi-turn jailbreaks.

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Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, Heyan Huang

DocFusion: A Unified Framework for Document Parsing Tasks (2025.findings-acl)

Challenge:	Existing methods to extract genre-specific and genre-agnostic features require great human effort.
Approach:	They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features.
Outcome:	The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres.

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Mingxu Chai, Ziyu Shen, Chong Zhang, Yue Zhang, Xiao Wang, Shihan Dou, Jihua Kang, Jiazheng Zhang, Qi Zhang

Challenge:	Existing methods for document parsing often employ multiple models, limiting performance . Existing models often employ discrete tokens, whereas recognition relies on continuous coordinates .
Approach:	They propose a Gaussian-Kernel Cross-Entropy Loss (GK-CEL) that unifies detection and recognition by enabling generative frameworks to handle both tasks simultaneously.
Outcome:	The proposed model performs competitively across four core document parsing tasks.

SciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing (2026.acl-long)

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Tong Zhang, Honglin Lin, Zhou Liu, Chong Chen, Wentao Zhang

Bi-Tuning with Collaborative Information for Controllable LLM-based Sequential Recommendation (2025.acl-long)

Challenge:	Existing benchmarks for scientific diagram generation rely on image-centric metrics or evaluation of intermediate symbolic representations rather than final rendered images.
Approach:	They propose a structure-first benchmark for evaluating scientific diagram generation from pixel-level outputs.
Outcome:	The proposed benchmark evaluates scientific diagram generation directly from pixel-level outputs.

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Xinyu Zhang, Linmei Hu, Luhao Zhang, Wentao Cheng, Yashen Wang, Ge Shi, Chong Feng, Liqiang Nie

HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2023.acl-long)

Challenge:	Existing approaches to optimize sequential recommendation systems rely on item ID sequences, but they lack collaborative knowledge and limited controllability.
Approach:	They propose a simple bi-tuning framework with collaborative information for controllable Large Language Model-based Sequential Recommendation (Laser) they incorporate learnable virtual tokens at prefix and suffix of input text to adapt LLMs with collaborative knowledge .
Outcome:	The proposed framework outperforms state-of-the-art recommendations on real-world datasets.

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He Zhu, Chong Zhang, Junjie Huang, Junran Wu, Ke Xu

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge.
Approach:	They propose a hierarchy-aware tree isomorphism network to enhance the text representations with only syntactic information of the label hierarchy.
Outcome:	The proposed model could boost the performance of hierarchical text classification without prior statistics or label semantics without prior data.

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi

Challenge:	Existing alignment paradigms for creative writing use static reward signals and supervised data.
Approach:	They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments.
Outcome:	The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references.

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

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for prompt injection have focused on optimizing the suffix, overlooking the role of the prompt.
Approach:	They propose a method that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies to optimize the suffix sequence for universal goal hijacking.
Outcome:	The proposed method can generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking.

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Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, Lingyao Li