Papers by Zhen Zhao

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model.
Approach:	They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences.
Outcome:	The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks.

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Shiting Huang, Zhen Fang, Zehui Chen, Siyu Yuan, Junjie Ye, Yu Zeng, Lin Chen, Qi Mao, Feng Zhao

CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset (2021.emnlp-demo)

Challenge:	a number of tools are used to perform complex tasks, but the tool utilization process can cause errors.
Approach:	They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks.
Outcome:	The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios.

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Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)

Challenge:	Existing crowd annotation tools for named entity recognition (NER) focus on efficiency and don't consider consistency of datasets.
Approach:	They propose a crowd annotation platform for Chinese named entity recognition (NER) CroAno provides a systematic solution for improving label consistency of Chinese NER datasets.
Outcome:	The proposed platform improves label consistency of Chinese NER datasets.

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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)

Challenge:	Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty.
Approach:	They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges.
Outcome:	The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods.

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Feiyang Wu, Peixin Huang, Yanli Hu, Zhen Tan, Xiang Zhao

Dynamic-prototype Contrastive Fine-tuning for Continual Few-shot Relation Extraction with Unseen Relation Detection (2025.coling-main)

Challenge:	Existing methods to model event associations struggle with semantic ambiguity and embedding bias.
Approach:	They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model .
Outcome:	The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism.

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Si Miao Zhao, Zhen Tan, Ning Pang, Wei Dong Xiao, Xiang Zhao

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)

Challenge:	Existing approaches to learn relations from labeled data overlook task interference in continual learning and memory requirements for different relations.
Approach:	They propose a framework to learn new relations from limited labeled data while preserving knowledge about previously learned relations.
Outcome:	The proposed framework is more practical and comprehensive for real-world scenarios.

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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang

A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)

Challenge:	Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed .
Approach:	They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management.
Outcome:	The proposed system automates human management by using a collaborative multi-agent system.

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Ting Xu, Haiqin Yang, Fei Zhao, Zhen Wu, Xinyu Dai

Low-Resource NLP

Challenge:	Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability.
Approach:	They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor.
Outcome:	The proposed method outperforms baseline methods by 6%-16% in F1 scores.

M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis (2023.emnlp-main)

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Fei Zhao, Chunhui Li, Zhen Wu, Yawen Ouyang, Jianbing Zhang, Xinyu Dai

Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)

Challenge:	Existing work mainly utilizes image information to improve the performance of MABSA task.
Approach:	They propose a multimodal Aspect-based Sentiment Analysis task that uses image information to improve model performance.
Outcome:	The proposed framework outperforms state-of-the-art work on three sub-tasks of MABSA.

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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen

Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)

Challenge:	Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped.
Approach:	They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation .
Outcome:	The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs.

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Zhigang Chen, Benjia Zhou, Jun Li, Jun Wan, Zhen Lei, Ning Jiang, Quan Lu, Guoqing Zhao

SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention (2025.acl-long)

Challenge:	Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient.
Approach:	They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve.
Outcome:	The proposed approach improves on three SLT datasets conducted under the gloss-free setting.

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Chengshuai Zhao, Zhen Tan, Chau-Wai Wong, Xinyan Zhao, Tianlong Chen, Huan Liu

IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review (2026.acl-long)

Challenge:	Content analysis is labor-intensive and time-consuming process that requires multiple rounds of manual annotation, domain expert discussion, and rule-based refinement.
Approach:	They propose a multi-agent framework that effectively Simulates Content Analysis via Large language model (LLM) ag Ents.
Outcome:	The proposed framework achieves human-approximated performance across various content analysis tasks.

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Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen, Zhen Xiang

Information Retrieval

Challenge:	Scientific research relies on accurate information retrieval from literature to support analytical decisions.
Approach:	They propose a task that automates fine-grained information retrieval faithfully grounded in the provided content in response to research-driven queries.
Outcome:	The proposed agent achieves 13.2% higher cross-domain accuracy than state-of-the-art RAG and research-agent baselines across seven backbone LLMs.

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models (2023.acl-demo)

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Shengding Hu, Ning Ding, Weilin Zhao, Xingtai Lv, Zhen Zhang, Zhiyuan Liu, Maosong Sun

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

Challenge:	Existing implementations that modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM have limited the practicality and flexibility of delta tuning.
Approach:	They propose an open-source library that provides a plug-and-play implementation of delta tuning methods for pre-trained models.
Outcome:	The proposed methods eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTM.

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

Multilinguality

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)

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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang

Forget the Unneeded: Backdooring Large Language Models via Contrastive-enhanced Machine Unlearning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference.
Approach:	They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes.
Outcome:	The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training.

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Shiji Yang, Shu Zhao, Congyao Mei, Zhen Yang, Jie Chen, Fulan Qian, Zhen Duan, Yanping Zhang

Robustness in NLP

Challenge:	Existing methods for prompt tuning for Large Language Models find backdoor attacks to be significant in data-rich scenarios.
Approach:	They propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios . they use a machine un learning method to capture precise backdoor patterns .
Outcome:	The proposed method captures precise backdoor patterns without association between triggers and backdoors, reducing side effects.

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)

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Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction (2026.findings-acl)

Challenge:	Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention.
Approach:	They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task.
Outcome:	The proposed framework outperforms state-of-the-art on two public datasets.

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Xu Shen, Qi Zhang, Song Wang, Zhen Tan, Xinyu Zhao, Laura Yao, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Kwonjoon Lee, Tianlong Chen

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)

Challenge:	Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors.
Approach:	They propose a metacognitive framework that enables step-level error detection and self-correction in Large Language Model based multi-agent systems (MAS) .
Outcome:	The proposed framework outperforms baselines on the Who When benchmark and delivers consistent gains on AgentErrorBench.

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Mengting Hu, Yinhao Bai, Yike Wu, Zhen Zhang, Liqi Zhang, Hang Gao, Shiwan Zhao, Minlie Huang

MathSight: A Benchmark Exploring Have Vision-Language Models Really Seen in University-Level Mathematical Reasoning? (2026.acl-long)

Challenge:	Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously .
Approach:	They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models .
Outcome:	The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets.

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Yuandong Wang, Yao Cui, Yuxin Zhao, Zhen Yang, Yangfu Zhu, Zhenzhou Shao

EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (2024.emnlp-main)

Challenge:	Existing benchmarks rarely isolate how much visual information contributes to reasoning . a growing collection of benchmarks has catalyzed rapid progress in multimodal reasoning - but how much it contributes remains unclear .
Approach:	They propose a university-level multimodal mathematical reasoning benchmark to quantify the effect of visual input.
Outcome:	The proposed benchmark disentangles and quantifies the effect of visual input on multimodal reasoning models.

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Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai

Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection (2022.findings-emnlp)

Challenge:	Existing methods to eliminate hallucinations require expensive human annotation . hallucination in multimodal large language models poses unique challenges for current research .
Approach:	They propose a fine-grained unlearning framework that performs gradient ascent to eliminate hallucinations without paired data.
Outcome:	The proposed method reduces hallucinations while preserving quality with modest computational overhead.

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Fei Zhao, Yuchen Shen, Zhen Wu, Xinyu Dai

RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)

Challenge:	Existing methods for ACD use label information of aspect categories to detect aspect categories . but, they still suffer from noise problems due to lack of supervised data .
Approach:	They propose a Label-Driven Denoising Framework to alleviate noise problems for ACD subtask . they use the label information of each aspect to generate a better prototype .
Outcome:	The proposed framework improves the performance of the multi-label few-shot Aspect Category Detection task.

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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai

CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)

Challenge:	RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images .
Approach:	They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a .
Outcome:	The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images.

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Weixin Zeng, Xiang Zhao, Jiuyang Tang, Zhen Tan, Xuqian Huang

Knowledge Representation

Challenge:	Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese .
Approach:	They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure .
Outcome:	The proposed corpus is based on 100 documents from diverse domains and is publicly accessible.

PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)

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Yi Zhao, Zhen Yang, Shuaiqi Duan, Wenmeng Yu, Zhe Su, Jibing Gong, Jie Tang

Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations (2022.coling-1)

Challenge:	PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements.
Approach:	They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance .
Outcome:	The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries.

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Jiazhan Feng, Chongyang Tao, Zhen Li, Chang Liu, Tao Shen, Dongyan Zhao

Information Retrieval

Challenge:	Recent work on grounding dialogue agents with knowledge documents has sparked increased attention . hand-labeling data to that end is time-consuming and many datasets lack knowledge annotations .
Approach:	They propose a reciprocal learning approach to optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
Outcome:	The proposed model outperforms previous state-of-the-art methods on two public benchmarks.

Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)

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Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

Challenge:	Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance.
Approach:	They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs.
Outcome:	The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs.

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)

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Chunhui Li, Yifan Wang, Zhen Wu, Zhen Yu, Fei Zhao, Shujian Huang, Xinyu Dai

Natural Language Interfaces

Challenge:	Text2SQL is a task that translates natural language into SQL statements.
Approach:	They propose a task that translates natural language into SQL statements.
Outcome:	The proposed task enables users to convert natural language into SQL statements.

MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing (2025.findings-acl)

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Yangbo Wei, Zhen Huang, Fangzhou Zhao, Qi Feng, Wei W. Xing

Dialogue Systems & Conversational Agents

Challenge:	Existing Large Language Models struggle to maintain emotionally consistent and psychologically plausible character personalities.
Approach:	They propose a framework that enhances LLMs’ ability to generate authentic personality-driven dialogues through stochastic emotional transitions.
Outcome:	The proposed framework achieves 93.3% emotional accuracy on the RAPD dataset and significantly outperforms existing approaches.

Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning (2026.acl-long)

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Qisheng Su, Shiting Huang, Zhen Fang, Ziyan Chen, Zehui Chen, Feng Zhao

Green & Sustainable NLP

Challenge:	Tool-Integrated Reasoning (TIR) is a tool that can be used to solve complex tasks.
Approach:	They propose a hardware-aware TIR-efficiency metric that unifies internal reasoning and external tool-use costs while explicitly accounting for non-reusable KV-Cache and long-tool-response scenarios.
Outcome:	The proposed metric explains wall-clock latency significantly better than token-count metric in a simulated high-concurrency industrial setting.

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026.findings-acl)

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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu

Reasoning

Challenge:	Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning from large language models (LLMs).
Approach:	They propose a data distribution lens to understand when and why CoT reasoning fails . they propose 'data-based' training that trains LLMs from scratch .
Outcome:	The proposed model enables models to generate reasoning trajectories that approximate those observed during training.

CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2022.findings-naacl)

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Zhen Li, Bing Xu, Conghui Zhu, Tiejun Zhao

Task-Aware Resolution Optimization for Visual Large Language Models (2025.emnlp-main)

Challenge:	Existing methods for multimodal sentiment detection do not consider token-level feature fusion.
Approach:	They propose a method for multimodal sentiment detection using a combination of text and image to encode and fuse token-level features.
Outcome:	The proposed method can fuse multimodal features with token-level features on three publicly available multimodal datasets.

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Weiqing Luo, Zhen Tan, Yifan Li, Xinyu Zhao, Kwonjoon Lee, Behzad Dariush, Tianlong Chen

Visual Data in NLP

Challenge:	Existing visual large language models pre-assume a fixed resolution for downstream tasks, leading to sub-optimal performance.
Approach:	They propose a formula to determine the optimal resolution for a given vision-language task . they then propose 'parameter-efficient' fine-tuning technique to extend the visual input resolution .
Outcome:	The proposed method is based on rigorous experiments on vision-language tasks.

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)

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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu

Challenge:	Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios .
Approach:	They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios.
Outcome:	The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm.

HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025.emnlp-main)

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An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, Weidong Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, Cheng-zhong Xu

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)

Challenge:	Mixture of Experts (MoE) models use homogeneous experts with diverse capacities, resulting in a lack of expert specialization and parameter utilization.
Approach:	They propose a framework where experts differ in size and possess diverse capacities . they propose HMoE to encourage frequent activation of smaller experts .
Outcome:	The proposed framework outperforms homogeneous homogenous MoE models on evaluation benchmarks and achieves lower loss rate with fewer activated parameters.

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Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen, Fei Zhao, Xinyu Dai

Sentiment Analysis

Challenge:	Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area.
Approach:	They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews.
Outcome:	The proposed dataset is manually annotated to better fit real-world scenarios.

FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)

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Zhen Li, Chongyang Tao, Jiazhan Feng, Tao Shen, Dongyan Zhao, Xiubo Geng, Daxin Jiang

Information Retrieval

Challenge:	Contemporary document ranking methods focus on transforming documents into passages to handle long inputs, but intensive query-irrelevant content may lead to harmful distraction and high query latency.
Approach:	They propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model.
Outcome:	Experiments on MS MARCO and TREC DL show that the proposed method is effective in document ranking tasks.

Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)

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Yao Wan, Yang Wu, Zhen Li, Guobiao Zhang, Hongyu Zhang, Zhou Zhao, Hai Jin, April Wang

Challenge:	Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages.
Approach:	They propose a sign language interface that enables the DHH community to engage more fully with data analysis.
Outcome:	The proposed interface can be used by the deaf and hard-of-hearing community.

LiPO: Listwise Preference Optimization through Learning-to-Rank (2025.naacl-long)

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Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J Liu, Xuanhui Wang

Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models (2024.emnlp-main)

Challenge:	Recent work on language models with curated feedback provides promising alternatives to RLHF . multiple responses can be ranked by reward models or AI feedback, but there is no study on directly fitting upon a list of responses.
Approach:	They propose a method that aligns language models with curated human feedback . they propose SLiC and DPO as promising alternatives to traditional RLHF .
Outcome:	The proposed method outperforms DPO and SLiC on several preference alignment tasks with curated and real rankwise preference data.

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Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Song Wang, Jundong Li, Tianlong Chen, Huan Liu

DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)

Challenge:	Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases.
Approach:	They propose to exploit openness of RAG models by injecting deceptive content into the retrieval database, intentionally changing the model’s behavior.
Outcome:	The proposed model can be exploited through crafted content uploads with access to the retriever.

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Menglong Lu, Zhen Huang, Yunxiang Zhao, Zhiliang Tian, Yang Liu, Dongsheng Li

DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)

Challenge:	Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model.
Approach:	They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set.
Outcome:	The proposed framework reduces label noise and preserves hard examples while maintaining accuracy.

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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources.
Approach:	They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing.
Outcome:	The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus.

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Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Zhuoyan Xu, Jiale Zhao, Zhen Zhuang

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)

Challenge:	a large computational cost for attention computation in large language models is a major obstacle .
Approach:	They propose a convolution-like structure for attention computation using convolution matrices . they then propose an efficient approximation method to approximate the attention matrix .
Outcome:	The proposed method achieves nearly linear time complexity in n1+o(1) time.

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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao

Attention Transfer Network for Aspect-level Sentiment Classification (2020.coling-main)

Challenge:	Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis.
Approach:	They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge.
Outcome:	The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks.

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Fei Zhao, Zhen Wu, Xinyu Dai

T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)

Challenge:	Aspect-level sentiment classification aims to detect the sentiment polarity of a given opinion target in a sentence.
Approach:	They propose a novel attention transfer network which can exploit attention from document-level sentiment datasets to improve the attention capability of the aspect-level classification task.
Outcome:	The proposed method outperforms state-of-the-art methods on two ASC benchmark datasets.

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Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)

Challenge:	Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text .
Approach:	They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages.
Outcome:	The proposed framework beats all the current competitive baselines on eight benchmark datasets, obtaining the best performance of unified NER.

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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui

ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)

Challenge:	Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions?
Approach:	They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods.
Outcome:	The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player.

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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin

Multilinguality

Challenge:	Mainstream research in natural language processing has focused on high-resource and modern languages.
Approach:	They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model .
Outcome:	The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer.

Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)

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Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, Weidong Xiao

Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA (2026.acl-long)

Challenge:	Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles.
Approach:	They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE.
Outcome:	The proposed system outperforms existing methods and is more powerful for sentences with multiple events.

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Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen, Zhen Duan, Shu Zhao

Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation (2026.findings-acl)

Challenge:	Existing knowledge base question answering methods are limited by syntactic constraints and are prone to structural deviations that render queries unexecutable.
Approach:	They propose a framework that reframes semantic parsing as an iterative reasoning process driven by execution feedback.
Outcome:	The proposed method achieves significant improvements in query executability and answer accuracy on the WebQSP and CWQ datasets.

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Qiong Wu, Tan Yue, Jianxin Liang, Zhen Li, Kai He, Shuai Zhao, Dongyan Zhao

Question Answering

Challenge:	Existing evaluation methods rely on rule-based matching with shallow semantic understanding or adopt LLM-as-a-Judge approaches that incur high cost and latency while offering limited error interpretability.
Approach:	They propose a curriculum learning based hierarchical framework for QA task evaluation that supports quick scoring and fine-grained error analysis.
Outcome:	The proposed framework outperforms baseline methods on quick scoring and error analysis tasks while being 25 faster.

Large Margin Representation Learning for Robust Cross-lingual Named Entity Recognition (2025.acl-long)

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Guangcheng Zhu, Ruixuan Xiao, Haobo Wang, Zhen Zhu, Gengyu Lyu, Junbo Zhao

Robustness in NLP

Challenge:	Existing approaches to name entity recognition neglect distribution skewness and pseudo-label bias . despite promising results, current approaches neglect these problems .
Approach:	They propose a framework that optimizes an adaptively reweighted contrastive loss to handle class skewness and pseudo-label bias.
Outcome:	The proposed framework outperforms existing methods on multiple benchmarks.

CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)

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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao

GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)

Challenge:	Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios.
Approach:	They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions.
Outcome:	The proposed framework improves generalization and realism of large language models under complex and irregular table conditions.

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Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao