Papers by Yang Cui

ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework (2025.acl-long)

Challenge:	Large pre-trained Vision-Language Models (VLMs) have revolutionized downstream vision-language tasks including classification, object detection, and segmentation.
Approach:	They propose to search for text prompts at the word level rather than optimizing continuous textual embeddings to boost adversarial robustness.
Outcome:	Experiments show that the proposed method outperforms hand-engineered prompts with average gains of +4.9% and +5.8%.

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Jiahao Yuan, Zixiang Di, Zhiqing Cui, Guisong Yang, Usman Naseem

Text Generation

Challenge:	Existing models for empathetic dialogue generation neglect the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy.
Approach:	They propose a framework that integrates emotion contagion and intent mimicry to enhance empathetic response generation.
Outcome:	The proposed framework outperforms existing models in relevance, controllability, and informativeness.

Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)

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Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu

Green & Sustainable NLP

Challenge:	Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space .
Approach:	They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space .
Outcome:	The proposed approach improves on existing methods in the latent space of text.

FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)

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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong

Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)

Challenge:	Large language models exhibit significant performance discrepancies between high- and low-resource languages.
Approach:	They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset.
Outcome:	The proposed model achieves consistent multilingual representations across languages.

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Ziqing Yang, Yiming Cui, Xin Yao, Shijin Wang

Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)

Challenge:	Pre-trained language models are computationally expensive and slow in inference due to their large sizes.
Approach:	They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models.
Outcome:	The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance.

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Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)

Challenge:	To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors.
Approach:	They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections.
Outcome:	The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule.

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Youquan Li, Miao Zheng, Fan Yang, Guosheng Dong, Bin Cui, Weipeng Chen, Zenan Zhou, Wentao Zhang

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information (2023.acl-long)

Challenge:	Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios.
Approach:	They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese.
Outcome:	The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios.

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Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, Xiaohui Cui

Natural Language Interfaces

Challenge:	Existing evaluation methods for open-domain dialogues are difficult due to the one-to-many issue of the open- domain dialogues.
Approach:	They propose a learning-based automatic evaluation metric which can robustly evaluate open-domain dialogues by augmenting CVAEs with a Next Sentence Prediction objective and employing Mutual Information to model the semantic similarity of text in the latent space.
Outcome:	The proposed method can evaluate open-domain dialogues on two open- domain dialogue datasets.

CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
Approach:	They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks.
Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

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Wei Zhang, Jian Yang, Jiaxi Yang, Ya Wang, Zhoujun Li, Zeyu Cui, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Code large language models (LLMs) enhance programming by understanding and generating code across languages.
Approach:	a new benchmark evaluates code understanding and generation in repositories using code large language models.
Outcome:	The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages.

CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation (2025.findings-acl)

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Guofeng Cui, Pichao Wang, Yang Liu, Zemian Ke, Zhu Liu, Vimal Bhat

Machine Translation

Challenge:	Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation remains challenging due to pretraining on predominantly English-centric datasets.
Approach:	They propose a method that combines reward scores with model confidence to improve model selection for fine-tuning.
Outcome:	The proposed method outperforms existing methods in translation accuracy and data efficiency.

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction (2023.findings-acl)

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Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li

Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values (2025.emnlp-main)

Challenge:	Empirical results show up to 11.74% absolute (20.97% relative) increase over unimodal baselines.
Approach:	They propose to patch the visual modality to the textual-established attribute in- formation extractor.
Outcome:	Empirical results show up to 11.74% absolute (29.9% relative) increase over unimodal baselines.

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Hongbo Zhang, Han Cui, Guangsheng Bao, Linyi Yang, Jun Wang, Yue Zhang

FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)

Challenge:	Existing offline preference optimization methods rely on preference labels to optimize large language models.
Approach:	They propose an offline method for enhancing large language models in reasoning tasks that utilizes value signals at individual reasoning steps.
Outcome:	The proposed framework outperforms offline preference optimization techniques by 4% to 6% on math reasoning, commonsense reasoning, and coding tasks.

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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang

TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models (2022.acl-demo)

Challenge:	Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation.
Approach:	a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 .
Outcome:	a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 .

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Ziqing Yang, Yiming Cui, Zhigang Chen

Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)

Challenge:	Large pre-trained language models have been used for many NLP tasks but computational resources are limited.
Approach:	They propose an open-source model pruning toolkit for pre-trained language models . they propose a self-supervised pruning method that can be applied without labeled data.
Outcome:	The proposed pruning method reduces model size without retraining the model and speeds up inference speed on the common CPU and GPU devices.

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Sihan Yang, Chenhang Cui, Zihao Zhao, Yiyang Zhou, Weilong Yan, Ying Wei, Huaxiu Yao

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning (2023.findings-acl)

Challenge:	Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing.
Approach:	They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources .
Outcome:	The proposed approach outperforms existing methods in reducing hallucinations and safety concerns.

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Zihang Xu, Ziqing Yang, Yiming Cui, Shijin Wang

Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)

Challenge:	Existing systems for logical reasoning have surpassed the average performance of humans in many tasks like SQuAD but there is still a long way to go when it comes to logical reasoning.
Approach:	They propose an InDicator-Oriented Logic Pre-training task which logically strengthens pre-trained models with the help of 6 types of logical indicators and a logicalally rich dataset.
Outcome:	The proposed task achieves state-of-the-art on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC.

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Leyang Cui, Sen Yang, Yue Zhang

Challenge:	Constituency parsers have been able to achieve competitive performance by using local features.
Approach:	They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents.
Outcome:	The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.

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

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

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)

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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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang

Challenge:	Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages.
Approach:	They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks.
Outcome:	The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B).

Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)

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Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, Ming Zhou

Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)

Challenge:	Experimental results show that unified model outperforms other models that treat encoding and matching separately.
Approach:	They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models .
Outcome:	The unified model outperforms models with Transformer layers on the machine reading comprehension task.

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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)

Challenge:	Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases.
Approach:	They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram.
Outcome:	The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries.

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Ziqing Yang, Yiming Cui, Chenglei Si, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)

Challenge:	Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations.
Approach:	They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions.
Outcome:	The proposed method is effective universally and further improves the performance of MRC tasks.

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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren

Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal (2024.acl-long)

Challenge:	Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance.
Approach:	They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability.
Outcome:	Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks.

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Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study (2026.acl-long)

Challenge:	Existing methods to train LLMs on previous training data are not feasible in real-world applications because of catastrophic forgetting.
Approach:	They propose a framework that uses the LLM to generate synthetic instances for rehearsal and refine the instance outputs based on the synthetic inputs.
Outcome:	The proposed framework achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient.

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Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang, Junxiao Yang, Qi Zhu, Shiyao Cui, Fei Mi, Lifeng Shang, Yingkang Wang, Hongning Wang, Minlie Huang

Guiding not Forcing: Enhancing the Transferability of Jailbreaking Attacks on LLMs via Removing Superfluous Constraints (2025.acl-long)

Challenge:	Large Reasoning Models have achieved remarkable success on reasoning-intensive tasks, but their enhanced reasoning capabilities do not translate to improved safety performance.
Approach:	They propose to use supervised fine tuning to enhance the safety of Large Reasoning Models.
Outcome:	The proposed method improves the safety of large reasoning models on reasoning-intensive tasks.

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Junxiao Yang, Zhexin Zhang, Shiyao Cui, Hongning Wang, Minlie Huang

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)

Challenge:	Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs).
Approach:	They propose a conceptual framework to elucidate transferability of gradient-based jailbreaking methods . they identify superfluous constraints as significant barriers to improved transferability .
Outcome:	The proposed method increases the overall transfer attack success rate (T-ASR) across target and source models with varying safety levels from 18.4% to 50.3% while improving stability and controllability of jailbreak behaviors.

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Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, Li Yuan

LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs (2024.findings-emnlp)

Challenge:	Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers.
Approach:	They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Outcome:	The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks.

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Sihui Yang, Keping Bi, Wanqing Cui, Jiafeng Guo, Xueqi Cheng

Question Answering

Challenge:	Non-factoid (NF) question answering is challenging to evaluate due to diverse potential answers and no objective criterion.
Approach:	They propose a listwise NFQA evaluation approach that uses Large Language Models to rank candidate answers in a descending list of reference answers sorted by descending quality.
Outcome:	The proposed method has higher correlations with human annotations than standard methods.

Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models (2026.acl-long)

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Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, Yang Wang

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Large language models (LLMs) have emerged as a promising avenue for time series forecasting . existing approaches face limitations such as marginalized role in model architectures and lack of interpretability.
Approach:	They propose a framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates.
Outcome:	The proposed model improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)

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Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen

Multilinguality

Challenge:	Existing multilingual pre-trained language models do not perform well on some low-resource languages.
Approach:	They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets .
Outcome:	The proposed model outperforms baseline models on various classification tasks.

Template-Based Named Entity Recognition Using BART (2021.findings-acl)

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Leyang Cui, Yu Wu, Jian Liu, Sen Yang, Yue Zhang

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings (2025.findings-acl)

Challenge:	Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters.
Approach:	They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework.
Outcome:	The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task.

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Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs.
Approach:	They propose a taxonomy of 3 perturbation strategies and 8 specific approaches in Chinese text to assess if they can detect perturbed Chinese toxic contents.
Outcome:	The proposed model can detect perturbed Chinese text with 8 different approaches . the proposed model is compared with 9 other LLMs from the US and China .

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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen

MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries (2023.acl-srw)

Challenge:	Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models.
Approach:	They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model.
Outcome:	The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD.

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Yang Cui, Lifeng Han, Goran Nenadic

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

Challenge:	Clinical texts contain important temporal information, such as medication start and end dates, appointment dates, and diagnosis dates.
Approach:	They propose to use prompt-based learning and fine-tuning to classify temporal relations between treatments and hospitalisation periods in discharge summaries.
Outcome:	The proposed method identifies whether a treatment was administered between the time of admission and discharge from the hospital.

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

InferPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents (2026.findings-acl)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Yixin Wu, Rui Wen, Chi Cui, Michael Backes, Yang Zhang

Robustness in NLP

Challenge:	Inference attacks are important for assessing model's robustness, but their implementation and parameters are challenging for non-experts.
Approach:	They propose an autonomous agent capable of conducting inference attacks without human intervention.
Outcome:	The proposed agent achieves a 100.0% task completion rate and near-expert attack performance with an average token cost of only 0.627 per run.

Cross-domain Generalization for AMR Parsing (2022.emnlp-main)

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Xuefeng Bai, Sen Yang, Leyang Cui, Linfeng Song, Yue Zhang

LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)

Challenge:	Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input.
Approach:	They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing.
Outcome:	The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets.

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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

CLeVeR: Multi-modal Contrastive Learning for Vulnerability Code Representation (2025.findings-acl)

Challenge:	Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks.
Approach:	They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework.
Outcome:	The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks.

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Jiayuan Li, Lei Cui, Sen Zhao, Yun Yang, Lun Li, Hongsong Zhu

Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021.acl-long)

Challenge:	Existing methods for detecting code capture the overall semantics of the code rather than its intrinsic vulnerability-specific semantics.
Approach:	They propose an approach that leverages contrastive learning to generate precise vulnerability code representations under the supervision of vulnerability descriptions.
Outcome:	The proposed approach outperforms state-of-the-art methods in vulnerability detection tasks by 11.85% and 13.61%.

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Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjie Yi, Yanghua Xiao

Information Extraction & Text Mining

Challenge:	Existing methods to extract relationships from texts depend on memory size and replay these memorized samples in subsequent tasks.
Approach:	They propose to use a model to extract relations between entities from texts where the samples of different relations are delivered into the model continuously.
Outcome:	The proposed model outperforms the state-of-the-art models and avoids catastrophic forgetting.

Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)

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Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu

Text Generation

Challenge:	Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss.
Approach:	They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence.
Outcome:	The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks.

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang, Zhoujun Li, Dongdong Zhang, Zheng Cui

Multilinguality

Challenge:	Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world.
Approach:	They propose to train different MMT models to support translations between different languages.
Outcome:	The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages.

FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)

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Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang

Low-Resource NLP

Challenge:	Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples.
Approach:	They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain.
Outcome:	The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods.

FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting (2026.findings-acl)

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Fengxian Ji, Jingpu Yang, Zirui Song, Yuanxi Wang, Zhexuan Cui, Yuke Li, Qian Jiang, Xiuying Chen

Challenges to Open-Domain Constituency Parsing (2022.findings-acl)

Challenge:	FineState-Bench evaluates whether an agent can correctly ground an instruction to the intended UI control and reach the exact target state.
Approach:	They propose a benchmark that evaluates whether an agent can correctly ground an instruction to the intended UI control and reach the exact target state.
Outcome:	The proposed benchmark evaluates whether an agent can ground an instruction to the intended UI control and reach the exact target state.

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Sen Yang, Leyang Cui, Ruoxi Ning, Di Wu, Yue Zhang

Challenge:	Existing findings on cross-domain constituency parsing are only made on a limited number of domains.
Approach:	They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features.
Outcome:	The proposed model significantly improves the performance of the proposed model on the domain-variant features.

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025.acl-long)

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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing evaluations of Large Language Models (LLMs) focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user’s perspective.
Approach:	They propose a Chinese Comprehensive Constraints Following Benchmark for LLMs that compiles constraints from real-world instructions and constructs a systematic framework for constraint types.
Outcome:	The proposed framework integrates multi-dimensional assessment criteria with requirement prioritization, covering various perspectives of constraints, instructions, and requirement fulfillment.

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Multilinguality

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)

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Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan, Chunjie Yang, Qing Cui, Jun Zhou

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Challenge:	Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets.
Approach:	They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC).
Outcome:	The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset.

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin

Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

Challenge:	Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages .
Approach:	They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs.
Outcome:	Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages.

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Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang

MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach:	They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome:	The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)

Challenge:	Existing research has focused on evaluating detection methods for specific domains or language models.
Approach:	They build a testbed to detect texts from diverse human writings and LLMs using different detection methods.
Outcome:	Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang

Benchmarking Robustness of Machine Reading Comprehension Models (2021.findings-acl)

Challenge:	Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text.
Approach:	They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks.
Outcome:	The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average.

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Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

Robustness in NLP

Challenge:	Existing benchmarks only evaluate models' robustness under test-time perturbations or adversarial attacks.
Approach:	They propose a model-agnostic benchmark to evaluate models' robustness under adversarial attacks.
Outcome:	The proposed model-agnostic benchmark evaluates models under four different types of adversarial attacks.

Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs (2025.emnlp-main)

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Dingdong Wang, Junan Li, Mingyu Cui, Dongchao Yang, Xueyuan Chen, Helen M. Meng

Rethinking Assessments of Prompt Injection Attacks (2026.findings-acl)

Challenge:	Speech Large Language Models (SpeechLLMs) have emerged as dominant speech processing approaches.
Approach:	They compare self-supervised learning-based discrete and continuous features . they compare performance across six spoken language understanding-related tasks .
Outcome:	The proposed models outperform discrete tokens and continuous features in six spoken language understanding-related tasks.

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Chi Cui, Yixin Wu, Michael Backes, Yang Zhang

GUITester: Enabling GUI Agents for Exploratory Defect Discovery (2026.findings-acl)

Challenge:	Prompt injection attacks are recognized as one of the primary risks faced by LLM-integrated applications in recent years.
Approach:	They evaluate prompt injection attacks on LLM-integrated applications across 37 target tasks, 185 injected tasks, 21 attack instructions, and 143,745 queries.
Outcome:	The proposed framework provides a solid foundation for assessing vulnerabilities in LLM-integrated applications and evaluating the efficacy of defensive strategies.

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Yifei Gao, Jiang Wu, Xiaoyi Chen, Yifan Yang, Zhe Cui, Tianyi Ma, Jiaming Zhang, Jitao Sang

Challenge:	Exploratory GUI testing is essential for software quality but suffers from high manual costs.
Approach:	They propose a framework that decouples navigation from verification via two modules . they propose 143 tasks and a GUITestBench benchmark that features 26 defects .
Outcome:	The proposed framework outperforms state-of-the-art benchmarks in 143 tasks and 26 defects.

What Have We Achieved on Text Summarization? (2020.emnlp-main)

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Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang

Information Extraction & Text Mining

Challenge:	Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals.
Approach:	They analyze 8 major sources of errors on 10 representative summarization models manually.
Outcome:	Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models.

MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents (2026.acl-long)

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Xiangyu Wu, Yuwei Hu, Tianyu Cui, Yueying Tian, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yang Yang, Jianfeng Lu

New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs (2026.acl-long)

Challenge:	Existing agent benchmarks fail to evaluate an agent's real-world capacity to handle CAPTCHA . Existing benchmarks ignore this practical challenge, failing to evaluate agents' ability to handle complex visual CAPTchas.
Approach:	They propose a benchmark annotated with Weighted Pass Rate and a new metric to measure agent's ability to handle CAPTCHA.
Outcome:	The proposed benchmark outperforms current state-of-the-art closed-source models on mirrorCAPTCHA and achieves 9.4% higher average weighted pass rate and 2.13% higher average Completion degree.

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Shiyao Cui, QingLin Zhang, Di Wang, Yida Lu, Zhexin Zhang, Jinhua Gao, Jinglin Yang, Min He, Han Qiu, Minlie Huang

Information Retrieval

Challenge:	Neologisms can foster new linguistic consensus by stabilizing shared meanings and usage in common communicative norms.
Approach:	They propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms . they propose 'SeTox' framework that integrates real-time web context for naeologim detection .
Outcome:	The proposed framework outperforms large-scale models in detecting neologism toxicity.

MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework (2025.findings-emnlp)

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Yupeng Qi, Ziyu Lyu, Min Yang, Yanlin Wang, Lu Bai, Lixin Cui

Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs (2025.findings-naacl)

Challenge:	Recent studies address safety-constrained online and offline preferences optimizations, but offline methods perform poorly in adaptively balancing safety and helpfulness.
Approach:	They propose a mixture of experts framework for safety-helpfulness dual Preference Optimization . they combine a single-preference enhanced direct preference optimization approach with a dynamic routing mechanism .
Outcome:	The proposed framework outperforms state-of-the-art methods in safety and helpfulness.

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Sen Yang, Xin Li, Leyang Cui, Lidong Bing, Wai Lam

Explainability & Interpretability in NLP

Challenge:	a new framework for complex reasoning with LLMs is developed to improve reasoning proof accuracy and interpretability.
Approach:	They propose to use LLMs to generate search logs that can be interpreted into human-readable reasoning proofs.
Outcome:	The proposed framework improves reasoning accuracy but lacks interpretability due to black-box nature of the solvers.

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning (2024.findings-emnlp)

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Sen Yang, Leyang Cui, Deng Cai, Xinting Huang, Shuming Shi, Wai Lam

Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features (N19-2)

Challenge:	Large language models (LLMs) have shown remarkable capabilities to understand and generate human languages, supporting applications such as question answering, coding, and psychological counseling.
Approach:	They propose strategies to save annotation budgets while achieving competitive or even better performances for iterative preference learning.
Outcome:	The proposed methods save annotation budgets while achieving better performance.

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Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin

Challenge:	e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features.
Approach:	They propose a model that integrates recurrent neural networks with manually-engineered features to identify cases where the customer expresses such an intent.
Outcome:	The proposed model outperforms baseline models and provides better recall and triage for specialized agents.

The Best of Both Worlds: Combining Parallel and Sequential Inference Scaling via Aggregation Fine-Tuning (2026.findings-acl)

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Yafu Li, Zhilin Wang, Tingchen Fu, Ganqu Cui, Sen Yang, Yu Cheng

CodeV: Issue Resolving with Visual Data (2025.findings-acl)

Challenge:	Empirical results show that AFT-trained models achieve substantial gains with test-time scaling.
Approach:	They introduce a supervised fine-tuning paradigm where models synthesize multiple draft responses into a single, refined answer.
Outcome:	Empirical results show that AFT-trained models outperform baseline models while eliminating external guidance.

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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang

Making the Best Use of Review Summary for Sentiment Analysis (2020.coling-main)

Challenge:	Large Language Models (LLMs) have expanded to more complex repository-level tasks.
Approach:	They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues.
Outcome:	The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data.

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Sen Yang, Leyang Cui, Jun Xie, Yue Zhang

Sentiment Analysis

Challenge:	Existing methods for sentiment analysis of user reviews are limited to a few examples.
Approach:	They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary.
Outcome:	The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

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Zhenfei Yang, Beiming Yu, Yuan Cui, Shi Feng, Daling Wang, Yifei Zhang

Dialogue Systems & Conversational Agents

Challenge:	Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach:	They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome:	The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)

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Yue Cui, Junhui Zhu, Liner Yang, Xuezhi Fang, Xiaobin Chen, Yujie Wang, Erhong Yang