Papers by Cong Yu

Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)

Challenge:	Formal verification typically requires developers to write detailed formal specifications . a formal verification system that generates candidate specifications is costly and error-prone .
Approach:	They propose an LLM-driven neuro-symbolic demonstration system that reframes specification writing as constrained structured synthesis.
Outcome:	The proposed system reduces hallucinations and produces proof-ready annotations.

Copied to clipboard

Yufei Li, Xiao Yu, Yanghong Guo, Yanchi Liu, Haifeng Chen, Cong Liu

Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data (2023.acl-short)

Challenge:	Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge .
Approach:	They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging .
Outcome:	The proposed framework outperforms baseline models on two benchmark datasets with better interpretability.

Copied to clipboard

Yufei Li, Xiao Yu, Yanchi Liu, Haifeng Chen, Cong Liu

Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack (2024.acl-long)

Challenge:	Existing approaches to extract entity pairs and their relations from labeled data are noisy and expensive.
Approach:	They propose a bootstrap learning approach that is motivated by intuition that the higher the uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths.
Outcome:	The proposed method outperforms baselines and related methods on two large datasets.

Copied to clipboard

Yu Fu, Yufei Li, Wen Xiao, Cong Liu, Yue Dong

Text Generation

Challenge:	Recent developments in balancing usefulness and safety of large language models raise a critical question . current attacks, especially adversarial ones that manipulate malicious prompts, often aim to manipulate the input .
Approach:	They show that LLMs can effectively summarize malicious long documents but often refuse to translate them.
Outcome:	The findings highlight a vulnerability in LLMs that can't translate or summarize documents . the study focuses on LLM models, Gemini and GPT-4, which can' be exploited .

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

Copied to clipboard

Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding (2026.acl-long)

Copied to clipboard

Diandian Guo, Cong Cao, Fangfang Yuan, Pin Xu, Cheng Hu, Zhicheng Zhang, Yu Liu, Yanbing Liu

Sentiment Analysis

Challenge:	Existing benchmarks for multimodal satirical cognition hinder evaluation of multimodal Sarcasm Understanding . lack of a unified benchmark for holistic satire cognition hampers evaluation of MSU .
Approach:	They propose a framework to decouple experts into orthogonal shared perception and private execution streams to physically block gradient interference between tasks.
Outcome:	The proposed framework achieves superior performance on DocMSU-PLUS.

Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)

Copied to clipboard

Bowen Yang, Cong Han, Yu Li, Lei Zuo, Zhou Yu

Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)

Challenge:	Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking.
Approach:	They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation.
Outcome:	The proposed architecture improves the integration of recommendation and dialog generation functions.

Copied to clipboard

Yu Fu, Cong Cao, Yuling Yang, Yuhai Lu, Fangfang Yuan, Dakui Wang, Yanbing Liu

Visual Data in NLP

Challenge:	Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text.
Approach:	They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level.
Outcome:	The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters.

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

Copied to clipboard

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 .

Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)

Copied to clipboard

Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang

Robustness in NLP

Challenge:	Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience.
Approach:	They propose a new algorithm that uses a random sampling algorithm to control risk.
Outcome:	The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms.

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)

Copied to clipboard

Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

Challenge:	Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations.
Approach:	They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models.
Outcome:	The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets.

Copied to clipboard

Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Challenge:	Scientific data visualization is an essential process in research, but its use of large language models remains unexplored.
Approach:	They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks.
Outcome:	The proposed framework improves performance of commercial and open-source models.

Copied to clipboard

Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification (2023.findings-acl)

Copied to clipboard

Kaihao Guo, Hang Yu, Cong Liao, Jianguo Li, Haipeng Zhang

Low-Resource NLP

Challenge:	Existing methods for text classification use only encoders or decoders that do not allow for the use of labels in unseen domains.
Approach:	They propose an autoencoder that encodes text into two disentangled spaces and decodes it to generate text with labels in the unseen domains.
Outcome:	The proposed model outperforms the existing methods in label-partially-unseen and label-fully-un-seeen scenarios and even outperfects the SOTA methods.

VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy (2026.findings-eacl)

Copied to clipboard

Yu Cui, Sicheng Pan, Yifei Liu, Haibin Zhang, Cong Zuo

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have been widely deployed in Conversational AIs . however, the methods proposed in the study rely on a white-box setting .
Approach:	They propose an indirect prompt injection attack that induces privacy extraction in LLMs . they use token-efficient data containing false memories to inject LLM data .
Outcome:	The proposed method outperforms baselines and achieves state-of-the-art performance.

Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)

Copied to clipboard

Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu

Sentiment Analysis

Challenge:	Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments.
Approach:	They propose a method that integrates multimodal incongruities via contrastive learning for multimodal sarcasm detection by using three views to drive multi-view learning.
Outcome:	The proposed method outperforms existing methods on benchmark datasets and shows that it is more generalizable than existing methods.

AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)

Copied to clipboard

Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun

Robustness in NLP

Challenge:	Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor.
Approach:	They propose a reward-based generalizable reward model to guide the policy model for effective test-time search.
Outcome:	The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average.

Improving Multilingual Sign Language Translation with Automatically Clustered Language Family Information (2025.coling-main)

Copied to clipboard

Ruiquan Zhang, Cong Hu, Pei Yu, Yidong Chen

Multilinguality

Challenge:	Recent research has focused on bilingual translation models, but multilingual sign language translation presents unique challenges due to the diversity of sign languages across nations.
Approach:	They propose a method that leverages sign language families to improve MSLT performance.
Outcome:	The proposed approach can achieve balance between translation accuracy and computational cost by regulating the number of language families.

Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)

Copied to clipboard

Yupu Liang, Yaping Zhang, Cong Ma, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)

Challenge:	Existing TIMT tasks focus on text-line-level images.
Approach:	They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation.
Outcome:	The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation.

Copied to clipboard

Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

Training ELECTRA Augmented with Multi-word Selection (2021.findings-acl)

Challenge:	Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks.
Approach:	They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm.
Outcome:	The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.

Copied to clipboard

Jiaming Shen, Jialu Liu, Tianqi Liu, Cong Yu, Jiawei Han

Language Models

Challenge:	Existing pre-training methods for NLP tasks require massive computation resources.
Approach:	They propose a method that trains a discriminator to detect replaced tokens and select original tokens from candidate sets.
Outcome:	The proposed method improves ELECTRA based on multi-task learning on GLUE and SQUAD datasets.

Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (2021.findings-acl)

Copied to clipboard

Xin Cong, Shiyao Cui, Bowen Yu, Tingwen Liu, Wang Yubin, Bin Wang

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)

Challenge:	Existing approaches to event detection ignore the trigger discrepancy and cause errors.
Approach:	They propose a unified model which converts a few-shot tagging problem into a single-shot model by using a Gaussian distribution.
Outcome:	The proposed model performs better than existing identifythen-classify models on a few-shot tagging problem with a double-part taging scheme.

Copied to clipboard

Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen

Responsible & Trustworthy NLP

Challenge:	Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer.
Approach:	They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs).
Outcome:	The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation.

CCIM: Cross-modal Cross-lingual Interactive Image Translation (2023.findings-emnlp)

Copied to clipboard

Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao, Yu Zhou, Chengqing Zong

Multilinguality

Challenge:	Existing research on text image machine translation (TIMT) lacks recognized source language information resulting in a decrease in translation performance.
Approach:	They propose a cross-modal cross-lingual interactive model which incorporates source language information by synchronizing source and target language results.
Outcome:	The proposed model outperforms end-to-end models and has faster decoding speed with smaller model size than cascade models.

Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation (2024.lrec-main)

Copied to clipboard

Cong Ma, Yaping Zhang, Zhiyang Zhang, Yupu Liang, Yang Zhao, Yu Zhou, Chengqing Zong

Conformal Event Prediction with Temporal Knowledge Graph (2026.findings-acl)

Challenge:	Existing research on text image machine translation (TIMT) is divided into two types: Cascade methods combine text image recognition and MT models to recognize source language text images.
Approach:	They propose a method which is optimized with hierarchical parental supervision to improve translation performance.
Outcome:	The proposed method significantly outperforms existing methods on synthetic and real-world tests on both synthetic and realistic images.

Copied to clipboard

Cheng Hu, Cong Cao, Fangfang Yuan, Diandian Guo, Pin Xu, Yu Liu, Yanbing Liu

Knowledge Representation

Challenge:	Current event prediction methods lack rigorous uncertainty quantification, which limits their reliability for decision-making.
Approach:	They propose a conformal prediction framework that applies conformal predictions to event prediction to address this challenge.
Outcome:	The proposed framework guarantees coverage while improving efficiency on three public datasets.

Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)

Copied to clipboard

Yingfa Chen, Chenlong Hu, Cong Feng, Chenyang Song, Shi Yu, Xu Han, Zhiyuan Liu, Maosong Sun

Syntactic Text Processing

Challenge:	Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered .
Approach:	They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts.
Outcome:	The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script.

DocHieNet: A Large and Diverse Dataset for Document Hierarchy Parsing (2024.emnlp-main)

Copied to clipboard

Hangdi Xing, Changxu Cheng, Feiyu Gao, Zirui Shao, Zhi Yu, Jiajun Bu, Qi Zheng, Cong Yao

Challenge:	Existing methods for document hierarchy parsing are limited due to the small scale and inconsistency of datasets.
Approach:	They propose a document hierarchy parsing dataset to compensate for the data scarcity problem and propose 'dHP' framework to grasp fine-grained text content and coarse-grounded pattern at layout element level.
Outcome:	The proposed framework grasps both fine-grained text content and coarse-grounded pattern at layout element level, enhancing the capacity of pre-trained text-layout models in handling multi-page and multi-level challenges.

AgreeSum: Agreement-Oriented Multi-Document Summarization (2021.findings-acl)

Copied to clipboard

Richard Yuanzhe Pang, Adam Lelkes, Vinh Tran, Cong Yu

Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)

Challenge:	Existing studies on agreement-oriented multidocument summarization have focused on clusters of articles . a recent study focused on the use of a pretraining framework to summarize articles based on the "union" of the articles.
Approach:	They propose to use agreement-oriented multidocument summarization to provide agreement-orientated summaries that represent information common to all articles.
Outcome:	The proposed task is called agreement-oriented multidocument summarization . the authors apply the pretrained model PEGASUS onto the task .

Copied to clipboard

Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, Zhen Fang

Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation (2026.acl-demo)

Challenge:	Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t .
Approach:	They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones.
Outcome:	The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets.

Copied to clipboard

Mingyu Wang, Bin Yu, Wenjian Lu, Zhi Wang, Gao Kefeng, Cheng Wen, Xu Lu, Cong Tian

Language Models

Challenge:	Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices.
Approach:	They present a tool that generates QEMU-based virtual devices directly from Linux driver source code.
Outcome:	The proposed tool generates QEMU-based virtual devices directly from Linux driver source code.

Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)

Copied to clipboard

Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li

Text Clustering

Challenge:	Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data.
Approach:	They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy.
Outcome:	The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks.

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

Copied to clipboard

Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun