Papers by Jiahao Zhang

G-LoRA: Global-Local Decoupled Low-Rank Adaptation (2026.findings-acl)

Challenge:	Existing methods to improve LLMs’ logical capabilities involve traceable or verifiable logical sequences that generate more reliable responses yet increase computational costs, or introduce rigid logic template rules, reducing flexibility.
Approach:	They propose a plug-and-play reasoning framework that enhances LLMs' logical reasoning abilities during the warm-up phase prior to batch inference.
Outcome:	The proposed framework surpasses baselines in both reasoning accuracy and efficiency.

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Jiahao Xiong, Yihong Huang, Yihe Liu, Xianming Hu, Hongbo Zhao, Kai Zhang

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) improves the fine-tuning efficiency and performance of large language models.
Approach:	They propose a low-rank adaptive approach that decomposes update matrix into global and local adapters and assigns them to local and global adapters.
Outcome:	The proposed method achieves up to 2.7% accuracy improvement over LoRA and its variants on commonsense reasoning, mathematical reasoning, and code generation.

LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection (2026.acl-long)

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Xin Wang, Jiahao Li, Licheng Zhang, Zhendong Mao

From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

Challenge:	Large Language Models suffer from hallucinations, severely undermining their reliability.
Approach:	They propose a framework that localizes fact-critical tokens and performs sequential analysis on their hidden states.
Outcome:	The proposed framework localizes fact-critical tokens using Factual Criticality . it then performs a focused sequential analysis on their hidden states .

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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

Multimodality

Challenge:	Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach:	They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome:	The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.

Draft Model Knows When to Stop: Self-Verification Speculative Decoding for Long-Form Generation (2025.emnlp-main)

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Ziyin Zhang, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Rui Wang, Zhaopeng Tu

Feature-Adaptive and Data-Scalable In-Context Learning (2024.acl-long)

Challenge:	Conventional speculative decoding methods use a predefined length policy for proposing drafts, but the reality deviates from this assumption.
Approach:	They propose a self-verification length policy that adaptively determines the lengths of draft sequences by referring to the draft entropy.
Outcome:	The proposed method achieves 17% speedup on MT-Bench and 22% speedup in long-form reasoning.

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Jiahao Li, Quan Wang, Licheng Zhang, Guoqing Jin, Zhendong Mao

From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)

Challenge:	In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints.
Approach:	They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task.
Outcome:	The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale.

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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (2025.findings-emnlp)

Challenge:	Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time.
Approach:	They propose a framework that reframes language modeling as next-state prediction under interaction.
Outcome:	The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics .

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Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Xin Zhao

End-to-End Beam Retrieval for Multi-Hop Question Answering (2024.naacl-long)

Challenge:	Existing systems with opaque architectures are limiting deep search capabilities for web-augmented large language models.
Approach:	They propose a transparent and modular multi-agent framework to democratize deep search for LLMs.
Outcome:	The proposed framework outperforms open-source systems in deep reasoning tasks.

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Jiahao Zhang, Haiyang Zhang, Dongmei Zhang, Liu Yong, Shen Huang

Information Retrieval

Challenge:	Existing beam retrieval frameworks for multi-hop question answering were customized for two-hop questions and were poorly supervised.
Approach:	They propose an end-to-end beam retrieval framework for multi-hop question answering . they combine an encoder and two classification heads to optimize the retrieval process .
Outcome:	The proposed framework improves on MuSiQue-Ans and surpasses all previous retrievers on HotpotQA and achieves 99.9% precision on 2WikiMultiHopQA.

Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems (2026.acl-long)

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Shuhua Yang, Jiahao Zhang, Yilong Wang, Dongwon Lee, Suhang Wang

Beyond Human Labels: A Multi-Linguistic Auto-Generated Benchmark for Evaluating Large Language Models on Resume Parsing (2025.emnlp-main)

Challenge:	Existing attacks exploit leakage of retrieved subgraphs, leaving the security implications of structured knowledge representations unexplored.
Approach:	They propose a framework that leverages a novelty-guided exploration–exploitation strategy and external graph memory modules to extract a latent entity–relation graph.
Outcome:	The proposed framework outperforms baselines on medical, agriculture, and literary datasets under identical query budgets while maintaining high precision.

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Zijian Ling, Han Zhang, Jiahao Cui, Zhequn Wu, Xu Sun, Guohao Li, Xiangjian He

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

Challenge:	Efficient resume parsing is critical for global hiring, yet the lack of dedicated benchmarks for evaluating large language models (LLMs) on multilingual, structure-rich resumes hinders progress.
Approach:	They propose to use a human-in-the-loop pipeline to generate 2,500 synthetic resumes spanning 50 templates, 30 career fields, and 5 languages to evaluate large language models.
Outcome:	The proposed benchmarks show that the models perform poorly on multilingual resumes and lack of standardized templates.

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

Circuit Complexity Bounds for RoPE-based Transformer Architecture (2025.emnlp-main)

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

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Bo Chen, Xiaoyu Li, Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song, Jiahao Zhang

SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026.findings-acl)

Challenge:	Recent studies provide the circuit complexity bounds to Transformer-like architectures. position embedding has emerged as a crucial technique in modern large language models.
Approach:	They propose to use position embedding to improve Transformer-like architectures by analyzing their circuits and analyzing the results.
Outcome:	The proposed model is able to solve canonical tasks without embedding positional information.

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Biao Yi, Jiahao Li, Yiming Li, Yu He, Baolei Zhang, Zheli Liu, Dacheng Tao

Robustness in NLP

Challenge:	Existing membership inference attacks require access to complete logits, but such access is often unavailable in real-world deployments where only the generated text is exposed.
Approach:	They propose a surrogate-free label-only MIA approach that directly estimates token probabilities through Monte Carlo sampling of the target model.
Outcome:	The proposed approach outperforms existing label-only attacks and serves as a foundational density estimator in the label-exclusive setting.

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

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Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

Low-Resource NLP

Challenge:	Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples.
Approach:	They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Outcome:	The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks.

When Allies Turn Foes: Exploring Group Characteristics of LLM-Based Multi-Agent Collaborative Systems Under Adversarial Attacks (2025.findings-emnlp)

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Jiahao Zhang, Baoshuo Kan, Tao Gong, Fu Lee Wang, Tianyong Hao

Robustness in NLP

Challenge:	a new study examines the group characteristics of adversarial agents in multi-agent collaborative systems . collaborative agents are tasked with generating counterfactual answers to a given collaborative problem .
Approach:	They evaluate collaborative systems under adversarial attacks and propose methods to mitigate them . they also introduce a new metric to quantify the robustness of collaborative systems against such attacks .
Outcome:	The proposed method has been proven effective against adversarial attacks.

On Weaponization-Resistant Large Language Models with Prospect Theoretic Alignment (2025.coling-main)

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Zehua Cheng, Manying Zhang, Jiahao Sun, Wei Dai

LGSA: Label Geometry Structuring and Aligning for Hierarchical Text Classification (2026.acl-long)

Challenge:	Existing safeguards for large language models are inadequate for open-weight models as minimal fine-tuning can bypass them.
Approach:	They propose a framework that prioritizes maximizing generative utility rather than a singular optimization metric and integrates prospect theory into LLM training to strengthen LLMs against misuse and weaponization.
Outcome:	The proposed framework strengthens LLMs against misuse and weaponization while maintaining high performance even after extensive fine-tuning.

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Shuai Zhang, Weibo Xu, Jiahao Nie, Kecheng Huang

Information Retrieval

Challenge:	Existing hierarchical text classification methods use prompt tuning or contrastive learning to implicitly learn label embeddings for classification, but this method fails to model hierarchy-aware geometric relations among labels.
Approach:	They propose a two-stage framework that transforms the label hierarchy from an implicit prior into an explicit embedding by using a general orthogonal frame.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on three real-world HTC datasets.

Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge (2023.findings-emnlp)

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Jinyuan Li, Han Li, Zhuo Pan, Di Sun, Jiahao Wang, Wenkun Zhang, Gang Pan

MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models (2026.acl-long)

Challenge:	Existing methods to enhance textual entity prediction neglect the need for external knowledge or encounter high redundancy in the retrieved knowledge.
Approach:	They propose a framework that leverages ChatGPT as an implicit knowledge base and heuristically generates auxiliary knowledge for more efficient entity prediction.
Outcome:	The proposed framework outperforms state-of-the-art methods on two classic datasets and exhibits a stronger robustness and generalization capability.

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Chenyang Gu, Jiahao Cheng, Meicong Zhang, Pujun Zheng, Jinquan Zheng, Guoxiu He

ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)

Challenge:	Existing LLMs emulate human research workflows but lack scientific grounding . empirical results show that MoRI outperforms strong commercial LLM models .
Approach:	They propose a framework that explicitly learns scientific reasoning from research motivations to methodologies.
Outcome:	The proposed framework outperforms commercial LLMs and agentic baselines in novelty, technical rigor, and feasibility.

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Jiahao Bu, Lei Ren, Shuang Zheng, Yang Yang, Jingang Wang, Fuzheng Zhang, Wei Wu

Sentiment Analysis

Challenge:	Sentiment analysis is a key task in e-commerce to detect fine-to-coarse sentiment polarities.
Approach:	They propose to use a large-scale Chinese restaurant review dataset ASAP to investigate the sentiment polarities underlying user reviews.
Outcome:	The proposed model outperforms state-of-the-art models on both tasks.

CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)

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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu

CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)

Challenge:	Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding.
Approach:	They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning .
Outcome:	The proposed model can encode words into fine-grained representations without modification of production pipelines.

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Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong

Challenge:	CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.
Approach:	They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems.
Outcome:	The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.

ReMamba: Equip Mamba with Effective Long-Sequence Modeling (2025.findings-emnlp)

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Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao

EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)

Challenge:	Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models.
Approach:	They propose a model which incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead.
Outcome:	The proposed model improves on the LongBench and L-Eval benchmarks by 3.2 and 1.6 points and attains performance almost on par with same-size transformer models.

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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu

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

Challenge:	Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts.
Approach:	They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.
Outcome:	The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.

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

Localized Low-Rank Adaptation within Clustered Parameter Subspaces (2026.acl-long)

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

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Jiahao Xiong, Yihe Liu, Xianming Hu, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Kai Zhang

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) for large language models has been successful in various domains.
Approach:	They propose to perform low-rank updates within clustered parameter subspaces . they group rows/columns of update matrix into locally coherent, uncorrelated subspace blocks .
Outcome:	Empirical results show that low-rank Adaptation (LoRA) is better than global adaptations in various domains.

Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)

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Jiahao Ying, Wei Tang, Yiran Zhao, Yixin Cao, Yu Rong, Wenxuan Zhang

CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling (2024.findings-acl)

Challenge:	Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language.
Approach:	They propose a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of large language models by decomposing evaluation along dimensions of linguistic medium and cultural context.
Outcome:	The proposed framework allows for a nuanced analysis of LLMs’ ability to process questions within both native and cross-cultural contexts cross-lingually.

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Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang, Jingwei Zhu, Di Yang, Jiahao Zhao, Guancheng Ye, Chengming Li, Xiping Hu

Dialogue Systems & Conversational Agents

Challenge:	Existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence.
Approach:	They propose a report-based multi-turn dialogue reconstruction framework for Chinese psychological counseling that uses large language models to assist counseling.
Outcome:	The proposed framework is open-source and can be used in future research.

TWIST: Text-encoder Weight-editing for Inserting Secret Trojans in Text-to-Image Models (2025.acl-long)

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Xindi Li, Zhe Liu, Tong Zhang, Jiahao Chen, Qingming Li, Jinbao Li, Shouling Ji

Robustness in NLP

Challenge:	Existing Trojan attacks require extensive training data and poor generalization, limiting effectiveness and scalability.
Approach:	They propose a method for embedding Trojans into plugins using a single edit layer . they find that the method reduces modified parameters by 8-fold and cuts injection time to 25 seconds .
Outcome:	The proposed method achieves an average attack success rate of 91%, a 78% improvement over the state-of-the-art (SOTA) method.

SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia (2025.findings-naacl)

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Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing

Multilinguality

Challenge:	Large Language Models (LLMs) have shown remarkable performance across various English benchmarks, including both human exam datasets such as MMLU and instruction-following datasets.
Approach:	They introduce two new benchmarks to evaluate the capabilities of Large Language Models in Southeast Asian (SEA) application scenarios.
Outcome:	The proposed benchmarks show that they can discern LLM performance on SEA language tasks compared to their translated benchmarks.

Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)

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Ruichu Cai, Shengyin Yu, Jiahao Zhang, Wei Chen, Boyan Xu, Keli Zhang

Minimal Distillation Schedule for Extreme Language Model Compression (2024.findings-eacl)

Challenge:	Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI.
Approach:	They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector.
Outcome:	The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training.

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Chen Zhang, Yang Yang, Qifan Wang, Jiahao Liu, Jingang Wang, Wei Wu, Dawei Song

Lifting the Curse of Capacity Gap in Distilling Language Models (2023.acl-long)

Challenge:	Existing methods for teacher assistant-based distillation require multiple trials to find the optimal teacher assistant.
Approach:	They propose a method that allows scheduling of an optimal teacher assistant in just one trial . they show that student performance is positively correlated with the scale-performance tradeoff .
Outcome:	The proposed method can select the optimal teacher assistant in just one trial . it can be used to compare performance of student and teacher assistants on GLUE benchmarks.

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Chen Zhang, Yang Yang, Jiahao Liu, Jingang Wang, Yunsen Xian, Benyou Wang, Dawei Song

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention (2026.acl-long)

Challenge:	Existing studies have shown that pretrained language models require a tremendous amount of inference compute to perform.
Approach:	They propose to compress pretrained language models to small ones with a teacher-student paradigm to fill the capacity gap.
Outcome:	The proposed model achieves state-of-the-art performance at small FLOPs compared with competitive baselines.

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Yifan Zhang, Chen Huang, Yueke Zhang, Jiahao Zhang, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang

Multimodality

Challenge:	Code Language Models learn attention based on statistical input-output token correlations.
Approach:	They propose a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes.
Outcome:	The proposed model outperforms baselines in three languages, with gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization.

Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)

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Jiahao Wang, Wenjun Ke, Peng Wang, Hang Zhang, Dong Nie, Jiajun Liu, Guozheng Li, Ziyu Shang

Multimodality

Challenge:	Named entity recognition (NER) is a key task reliant on textual data.
Approach:	They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries.
Outcome:	The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets.

Exposing Privacy Risks in Graph Retrieval-Augmented Generation (2026.findings-acl)

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Jiale Liu, Jiahao Zhang, Suhang Wang

SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have limitations such as generating factually incorrect information (hallucinations) Retrieval-Augmented Generation (RAG) is a powerful paradigm for enhancing LLMs with external, up-to-date knowledge.
Approach:	They investigate the data extraction vulnerabilities of Graph RAG systems by executing tailored attacks on them.
Outcome:	The proposed attacks exploit the vulnerability of Graph RAG systems to leak raw text and structured data.

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Yuanyang Yin, Yaqi Zhao, Yajie Zhang, Yuanxing Zhang, Ke Lin, Jiahao Wang, Xin Tao, Pengfei Wan, Wentao Zhang, Feng Zhao

TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)

Challenge:	Multimodal Large Language Models (MLLMs) integrate visual and textual inputs, yet modality alignment remains one of the most challenging aspects.
Approach:	They propose a token-level supervision alignment method that enables more precise visual-text alignment during pretraining.
Outcome:	The proposed method improves performance across various model sizes, with smaller models benefiting the most.

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Yilin Cao, Jiahao Zhao, Ruike Zhang, Hanyi Zou, Wenji Mao

Text Generation

Challenge:	Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed .
Approach:	They propose a method that explicitly defines attribute relations as inhibtory for multi-attribute CTG . they propose 'tara' which employs token-level attribute relation adaptation and representation to generate text with the balanced multi-attribut .
Outcome:	The proposed method generates text with the balanced multi-attribute control.

Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)

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Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang

Syntactic Text Processing

Challenge:	Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets.
Approach:	They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data.
Outcome:	Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets.

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition (2024.findings-acl)

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Jinyuan Li, Han Li, Di Sun, Jiahao Wang, Wenkun Zhang, Zan Wang, Gang Pan

Visual Data in NLP

Challenge:	Existing methods for Grounded Multimodal Named Entity Recognition (GMNER) lack a strong correlation between image-text pairs and is ungroundable.
Approach:	They propose a framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models as a connecting bridge.
Outcome:	The proposed framework outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks.

Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)

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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah

ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning (2026.findings-acl)

Challenge:	Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns.
Approach:	They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP.
Outcome:	Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines.

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Jinyang Wu, Guocheng Zhai, Ruihan Jin, Jiahao Yuan, Yuhao Shen, Shuai Zhang, Zhengqi Wen, Jianhua Tao

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

Challenge:	Existing approaches to optimize large language models with external tools are limited.
Approach:	They propose a dual-path framework for dynamic tool usage in cross-domain complex reasoning . they exploit empirical priors for domain-specific alignment and RL-based multi-step routing .
Outcome:	The proposed framework outperforms closed-source models and existing methods on in-distribution and out-of-distortion tasks.

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

Syntactic Text Processing

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)

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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang

Structured Data in NLP

Challenge:	Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities.
Approach:	They propose a taxonomy spanning Graph-Assisted Knowledge Augmentation, Graph Assisted Reasoning and Planning, and Graphed LLM Collaboration.
Outcome:	The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings.

TombRaider: Entering the Vault of History to Jailbreak Large Language Models (2025.emnlp-main)

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Junchen Ding, Jiahao Zhang, Yi Liu, Ziqi Ding, Gelei Deng, Yuekang Li

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

Challenge:	Existing jailbreak techniques focus on prompt manipulation or intent obfuscation to bypass safety filters.
Approach:	They propose a jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of Large Language Models (LLMs) they use an inspector agent to extract historical information and an attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters.
Outcome:	The proposed jailbreak technique outperforms state-of-the-art jailbreak techniques on six popular models and maintains over 55.4% ASR against defence mechanisms.

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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025.acl-long)

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Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, Ping Luo