Papers by Jing Yang

Reinforcement Learning for Diffusion LLMs via Energy-Based Gibbs Alignment (2026.acl-long)

Challenge:	Social media has become a fertile ground for nurturing rumors and misinformation due to its lack of systematic moderation.
Approach:	They propose a framework to enhance the joint predictive capabilities of LLMs for stance detection and rumor verification tasks.
Outcome:	The proposed framework outperforms state-of-the-art methods and generalizes to non-LLMs accommodated as task models.

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Yijia Fan, Jing Yang, Mingyu Liu, Kaitong Cai, Jian Wang, Keze Wang, Jusheng Zhang

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)

Challenge:	Diffusion Large Language Models (dLLMs) offer parallel decoding and bidirectional context modeling . aligning dLLms with reinforcement learning (RL) remains a challenge .
Approach:	They propose a variational framework that reformulates RL for dLLMs as a distribution matching problem.
Outcome:	The proposed framework reformulates RL for dLLMs as a distribution matching problem.

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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear.
Approach:	They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer .
Outcome:	The proposed applications improve hallucination detection performance by integrating two different inputs.

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Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)

Challenge:	Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations.
Approach:	They propose a framework that covers 61 risk categories across four modality interactions to address this gap.
Outcome:	The proposed framework covers 61 risk categories across four distinct modality interactions.

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Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai

AnswerFact: Fact Checking in Product Question Answering (2020.emnlp-main)

Challenge:	Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive .
Approach:	They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model.
Outcome:	The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy.

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Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam

Question Answering

Challenge:	a product-related community question answering platform is widely employed in many E-commerce sites . however, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information.
Approach:	They propose a large scale fact checking dataset from product question answering forums to predict the answer veracity . each answer is accompanied by its veraity label and associated evidence sentences .
Outcome:	The proposed model outperforms baselines on the question veracity prediction task.

Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)

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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)

Challenge:	Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered.
Approach:	They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model.
Outcome:	The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications.

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Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, Jing Yang

Automated Progressive Red Teaming (2025.coling-main)

Challenge:	Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications.
Approach:	They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally.
Outcome:	The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance.

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Bojian Jiang, Yi Jing, Tong Wu, Tianhao Shen, Deyi Xiong, Qing Yang

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)

Challenge:	Automated red teaming (ART) is effective but time-consuming, costly and lacks scalability.
Approach:	They propose an automated red teaming framework that generates adversarial prompts to expose LLM vulnerabilities.
Outcome:	The proposed framework explores and exploits LLM vulnerabilities through multi-round interactions.

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Chen Liang, Jing Xu, Yangkun Lin, Chong Yang, Yongliang Wang

ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging (2026.acl-long)

Challenge:	Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields.
Approach:	They propose a novel GNN-based ERC model that captures speaker and position information.
Outcome:	The proposed model captures speaker and position-aware conversation structure information.

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Junyao Yang, Chen Qian, Wen Shen, Yong Liu, Jing Shao, Dongrui Liu

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

Challenge:	Existing models with long chain-of-thought reasoning lack reasoning depth and domain-specific utility.
Approach:	They propose a model merging framework that integrates reasoning with domain-specific task models.
Outcome:	The proposed model merging framework outperforms state-of-the-art models while maintaining robust reasoning performance.

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

Understanding GUI Agent Localization Biases through Logit Sharpness (2025.findings-emnlp)

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

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Xingjian Tao, Yiwei Wang, Yujun Cai, Zhicheng Yang, Jing Tang

Challenge:	Multimodal large language models often exhibit hallucinations that compromise reliability . despite promising performance, these models often display systematic localization errors .
Approach:	They propose a framework that categorizes model predictions into four distinct types . they propose metric that evaluates alignment between semantic continuity and logits distribution .
Outcome:	The proposed framework categorizes model predictions into four different types . it reveals nuanced failure modes beyond traditional accuracy metrics .

ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Explainability & Interpretability in NLP

Challenge:	Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity.
Approach:	They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor.
Outcome:	The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor.

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, Richard Yi Da Xu, Yunya Song, Xian Yang

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)

Challenge:	Existing models struggle to balance predictive accuracy with human-understandable rationales.
Approach:	They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation.
Outcome:	Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation.

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Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang

PsyChain: A Collaborative Chain-of-Agents Framework for Generating Personalized and Professional Counseling Dialogues (2026.findings-acl)

Challenge:	Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions.
Approach:	They propose a benchmark that tests multipanel visual reasoning models with 6,600 triplets of questions, answers, and multipanel images.
Outcome:	The proposed benchmark features 6,600 triplets of questions, answers, and multipanel images that challenge state-of-the-art Multimodal Large Language Models (MLLMs) human users can attain approximately 99% accuracy on these questions, compared with previous benchmarks.

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Yi Feng, Zijie Yang, Chen Zhang, Wenxuan Zhang, Dongming Zhang, Liping Jing, Jian Yu

Dialogue Systems & Conversational Agents

Challenge:	Existing psychological counseling datasets suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability.
Approach:	They propose a framework that evolves static counseling corpora into high-fidelity dialogues . they use a Client Profiler that pairs life scenarios with psychological personality archetypes based on client personality and stage progression .
Outcome:	The proposed framework achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling.

HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)

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Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

Unveiling Internal Reasoning Modes in LLMs: A Deep Dive into Latent Reasoning vs. Factual Shortcuts with Attribute Rate Ratio (2025.emnlp-main)

Challenge:	Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies.
Approach:	They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue.
Outcome:	The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance.

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Yiran Yang, Haifeng Sun, Jingyu Wang, Qi Qi, Zirui Zhuang, Huazheng Wang, Pengfei Ren, Jing Wang, Jianxin Liao

FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)

Challenge:	Existing research in multi-hop questions has identified two reasoning modes, but has not investigated how these modes differ during inference.
Approach:	They propose a classification metric that compares latent reasoning and factual shortcuts in multi-hop questions.
Outcome:	The proposed metric achieves 90% accuracy on the proposed datasets and demonstrates effectiveness in RAG conflict scenarios.

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Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua

NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)

Challenge:	Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs.
Approach:	They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction.
Outcome:	Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis.

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Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim

DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks (2022.findings-naacl)

Challenge:	Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations .
Approach:	They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions.
Outcome:	The proposed model achieves 55.5 exact match scores while human performance is 89.7.

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Ziyang Luo, Yadong Xi, Jing Ma, Zhiwei Yang, Xiaoxi Mao, Changjie Fan, Rongsheng Zhang

CoT-VTM: Visual-to-Music Generation with Chain-of-Thought Reasoning (2025.findings-acl)

Challenge:	Experimental results show that Transformer Encoder model can't automatically capture word order, so explicit position embeddings are required to be fed into the target model.
Approach:	They propose a Transformer-based language model DecBERT that uses a causal attention mask to capture word order.
Outcome:	The proposed model improves on the GLUE language understanding benchmark and accelerates the pre-training process.

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Xikang Guan, Zheng Gu, Jing Huo, Tianyu Ding, Yang Gao

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)

Challenge:	Existing methods for visual-to-music generation lack large-scale, high-quality visual-music paired datasets and lack of direct semantic correspondence between visuals and music.
Approach:	They propose a framework that distills Chain-of-Thought reasoning to enable visual-to-music generation without paired data.
Outcome:	The proposed framework achieves optimal performance on image-to-music and video-to music tasks.

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Jiangang Bai, Yujing Wang, Yiren Chen, Yaming Yang, Jing Bai, Jing Yu, Yunhai Tong

VQA-Augmented Machine Translation with Cross-Modal Contrastive Learning (2025.findings-emnlp)

Challenge:	Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
Approach:	They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers.
Outcome:	The proposed framework improves on pre-trained models on natural language understanding datasets and shows that it can be used to train pre-structured neural networks.

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Zhihui Zhang, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF (2025.findings-naacl)

Challenge:	Existing multimodal machine translation methods often extract visual features using pre-trained models while learning text features from scratch, leading to representation imbalance.
Approach:	They propose a cross-modal VQA-augmented multimodal machine translation method . it aligns image-source text pairs and image-question text pairs through dual-text contrastive learning .
Outcome:	The proposed method outperforms state-of-the-art methods on multiple evaluation metrics.

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Yuyan Bu, Liangyu Huo, Yi Jing, Qing Yang

Challenge:	Existing efforts to mitigate length bias in reward models have decreased accuracy . achieving an automatic proxy that perfectly replicates human judgment is challenging in practice.
Approach:	They propose an adaptive approach that dynamically adjusts the influence of response length in reward evaluations according to the context of the query.
Outcome:	The proposed approach reduces unnecessary verbosity while improving overall response quality.

Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)

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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song

How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning (2023.emnlp-main)

Challenge:	Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization.
Approach:	They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection.
Outcome:	The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues.

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Hang Chen, Xinyu Yang, Jing Luo, Wenjing Zhu

Representation Learning

Challenge:	Existing models excel at capturing semantic correlations within utterance embeddings but fail to determine specific causal relationships.
Approach:	They propose to incorporate i.i.d. noise terms into conversation process to build a structural causal model . they propose to use unstructured conversation data to facilitate deep learning .
Outcome:	The proposed approach can be implemented in unstructured conversation data and a synthetic dataset that includes i.i.d. noise.

Consistent Client Simulation for Motivational Interviewing-based Counseling (2025.acl-long)

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Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Phey Ling Kit, Jenny Giam Xiuhui, John Pinto, Ee-Peng Lim

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Existing approaches to simulate human clients in mental health counseling are limited and cost prohibitive.
Approach:	They propose a framework that supports consistent client simulation for mental health counseling by tracking the mental state of a simulated client, controlling its state transitions, and generating for each state behaviors consistent with the client’s motivation, beliefs, preferred plan to change, and and receptivity.
Outcome:	The proposed framework can simulate human clients for mental health counseling tasks and achieve higher consistency than previous methods.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)

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Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang

V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment (2026.findings-acl)

Challenge:	Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds.
Approach:	They propose a framework for self-referential leakage detection for gray-box and black-box settings.
Outcome:	The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines.

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Jing Wang, Yaomin Wu, Yinglin Wang, Yitong Yang

Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)

Challenge:	Current methods for steering large language models rely on prompt engineering or reasoning-time guidance.
Approach:	They propose a value-controllable pluralistic alignment framework enhanced with conditioned gating that dynamically directs the flow among multiple experts based on an input value or moral vector.
Outcome:	The proposed method outperforms prompt-based steering and multi-task PEFT benchmarks on two 8-billion-parameter backbones.

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Chunpu Xu, Jing Li, Piji Li, Min Yang

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)

Challenge:	Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging.
Approach:	They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests.
Outcome:	The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches.

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Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

Pengwei Zhan, Jing Yang, He Wang, Chao Zheng, Xiao Huang, Liming Wang

Challenge:	Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators.
Approach:	They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model .
Outcome:	The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels.

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MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling (2026.findings-acl)

Challenge:	Neural language models are vulnerable to word-level adversarial text attacks . previous word-based search methods assume important words influence prediction .
Approach:	They propose a method for similarizing the influence of words with contrast learning that encourages model to learn sentence representations in which words of varying importance have a more uniform influence on prediction.
Outcome:	The proposed method is compatible with various training methods and improves model robustness against various adversarial attacks.

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Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)

Challenge:	Existing reasoning large language models (LLMs) generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness.
Approach:	They propose a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation.
Outcome:	The proposed model achieves theory-of-mind assessment comparable to state-of the-art systems with an order of magnitude less computation.

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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang

UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Challenge:	Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE.
Approach:	They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event.
Outcome:	The proposed model outperforms the state-of-the-art models on four widely used datasets.

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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach:	They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome:	The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

Speaker Verification in Agent-generated Conversations (2024.acl-long)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in large language models have increased the capabilities of conversational AI to solve challenging dialogue problems.
Approach:	They propose a task to verify whether two sets of utterances originate from the same speaker.
Outcome:	The proposed task aims to verify whether two sets of utterances originate from the same speaker.

HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition (2021.findings-emnlp)

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Zhiwei Yang, Jing Ma, Hechang Chen, Yunke Zhang, Yi Chang

Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)

Challenge:	Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods.
Approach:	They propose a Hierarchical Transformer network which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner.
Outcome:	The proposed method achieves much better performance than the state-of-the-art approaches on GENIA, ACE-2004, ace-2005 and NNE datasets.

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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie

Challenge:	Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores.
Approach:	They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs.
Outcome:	The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values.

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)

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Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, Yi Chang

Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey (2024.naacl-long)

Challenge:	Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays.
Approach:	They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports.
Outcome:	The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives.

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Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao, Yu Qiao

A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) are now commonplace in conversation applications, but their misuse for generating harmful responses has raised serious societal concerns.
Approach:	They provide a comprehensive overview of recent studies covering attacks, defenses, and evaluations of Large Language Models (LLMs) .
Outcome:	The proposed review summarizes three aspects of LLM conversation safety: attacks, defenses, and evaluations.

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Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)

Challenge:	Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data.
Approach:	They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations.
Outcome:	Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models.

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Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, Yu Qiao

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)

Challenge:	Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model.
Approach:	They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights.
Outcome:	The proposed method matches or outperforms existing methods in safety alignment and long-form question answering.

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Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang

Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation (2025.findings-emnlp)

Challenge:	Existing LLMs are delicate and elusive in prompt words and styles.
Approach:	They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning .
Outcome:	The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality.

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Zhuang Yu, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media (2025.emnlp-main)

Challenge:	Multimodal Machine Translation (MMT) is effective in resolving linguistic ambiguities, but visual information often introduces redundancy or noise, potentially impairing translation quality.
Approach:	They propose a semantic-augmented framework that integrates "Imagination" and "Contemplation" they first generate synthetic images from source text and align them with authentic images via an optimal transport loss .
Outcome:	The proposed framework outperforms baselines on translation datasets with visually ambiguous or weakly correlated content.

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Yujie Wang, Yunwei Zhao, Jing Yang, Han Han, Shiguang Shan, Jie Zhang

PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)

Challenge:	Digital media platforms often contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains.
Approach:	They propose a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior.
Outcome:	The proposed framework lays the groundwork for scalable computational analysis of cognitive fixation.

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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu

Sentiment Analysis

Challenge:	Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction.
Approach:	They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise.
Outcome:	The proposed framework achieves state-of-the-art on three public datasets.

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)

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Kaiyuan Liu, Youcheng Pan, Yang Xiang, Daojing He, Jing Li, Yexing Du, Tianrun Gao

Programming Languages in NLP

Challenge:	Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities.
Approach:	They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction.
Outcome:	The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators.

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)

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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang

PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter (2023.emnlp-main)

Challenge:	Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress.
Approach:	They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability.
Outcome:	The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability.

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Haoyan Yang, Zhitao Li, Yong Zhang, Jianzong Wang, Ning Cheng, Ming Li, Jing Xiao

Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are too large to be fine-tuned with budget constraints and some are only accessible via APIs.
Approach:	They propose a pluggable Reward-Driven Contextual Adapter that integrates large language models as generators and trains them to refine the retrieved information.
Outcome:	The proposed method improves ReQA performance on three datasets by up to 20% compared to existing methods.

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Ke Yang, Shuguang Yuan, Jing Yu, Chi Chen

Culturally Aware Natural Language Inference (2023.findings-emnlp)

Challenge:	Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality.
Approach:	They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm.
Outcome:	The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure.

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Jing Huang, Diyi Yang

Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)

Challenge:	Cultural norms are behavioral rules and conventions shared within specific groups, connecting cultural symbols and values.
Approach:	They propose a task that operationalizes cultural variations in language understanding through a natural language inference task that surfaces cultural variations as label disagreement between annotators from different cultural groups.
Outcome:	The proposed model can be evaluated at which levels it is culturally aware.

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Chang Yang, Peng Zhang, Jing Zhang, Hui Gao, Changhao Song

MDTeamGPT: Mitigating Context Collapse and Enabling Self-Evolution in Medical Multi-Agent Reasoning (2026.findings-acl)

Challenge:	rumor detection has been reshaped by large language models (LLMs) this paper proposes a Cognition-Interaction-Behavior (CIB) framework for rumour detection based on collective intelligence .
Approach:	They propose a Cognition-Interaction-Behavior framework for rumor detection based on collective intelligence and explore synergistic relationship between LLMs and collective intelligence in rumour governance.
Outcome:	The proposed framework unifies existing methods and reveals synergistic relationship between LLMs and collective intelligence in rumor governance.

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Kai Chen, Xinfeng Li, Tianpei Yang, Hewei Wang, Guang Yang, Jing Huo, Yang Gao

QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)

Challenge:	Long, multi-round, multirole interaction trajectories lead to severe information dilution and context window overload, triggering context collapse which destabilizes reasoning.
Approach:	They propose a multi-agent framework that compresses and reorganizes multi-round consensus.
Outcome:	The proposed framework outperforms baselines across text-based and multimodal tasks while demonstrating superior diagnostic performance and stability in complex clinical scenarios.

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Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong Sun

Challenge:	Existing methods to recommend quotes are evaluated on unpublished datasets .
Approach:	They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese.
Outcome:	The proposed model outperforms existing methods on all three parts of QuoteR.

Nash-Pruned CredMAS: Dynamic Panel Pruning for VLM-MAS using Nash-based Selection and Doubly-Robust Credits (2026.findings-acl)

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Yijia Fan, Mingyu Liu, Jing Yang, Jian Wang, Keze Wang, Jusheng Zhang

Robustness in NLP

Challenge:	Multi-Agent Systems (MAS) are expensive due to static panel designs, where all N agents communicate at every T round.
Approach:	They propose an economic framework that transforms agent selection into a dynamic resource allocation game.
Outcome:	The proposed system reduces token consumption by over 25% on challenging benchmarks while reducing token consumption.

Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang

DMSD: Dual-Modal Semantic Disentanglement for Compositional Zero-Shot Learning (2026.findings-acl)

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Pan Yang, Jing Yang, Ruan Xiao li, Yuling Chen, Yuankai Wu, Quan Zhou, Xu Wang

Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)

Challenge:	Compositional Zero-Shot Learning (CZSL) is a new research paradigm that learns sub-concepts from seen compositions and recognizes unseen novel combinations.
Approach:	They propose a Dual-Modal Semantic Disentanglement framework that integrates visual and textual information to achieve effective sub-concept disentangling.
Outcome:	The proposed framework achieves state-of-the-art performance on three benchmark datasets . it integrates a class-centroid bridge module to guide class centroids toward the textual space .

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Yang Li, Guodong Long, Tao Shen, Jing Jiang

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)

Challenge:	Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems.
Approach:	They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling.
Outcome:	The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance.

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Ke Yang, Dongyang Liang, Jing Yu, Shuguang Yuan, Chi Chen

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning (2026.acl-long)

Challenge:	Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary.
Approach:	They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution.
Outcome:	The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity.

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Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)

Challenge:	Existing work on large reasoning models (LRMs) focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Approach:	They propose to use reinforcement learning to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Outcome:	The proposed model reduces token usage by around 50%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5b, while significantly improving accuracy.

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Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)

Challenge:	Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data.
Approach:	They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention.
Outcome:	The proposed neural network improves the state-of-the-art on the NYT dataset .

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Hongzhan Lin, Jing Ma, Mingfei Cheng, Zhiwei Yang, Liangliang Chen, Guang Chen

Structured Data in NLP

Challenge:	Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure.
Approach:	They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts.
Outcome:	The proposed method improves performance on three Twitter datasets and can detect rumors at early stages.

FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)

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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning (2024.emnlp-main)

Challenge:	Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers.
Approach:	They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model achieves competitive performance with frontier models while maintaining generation efficiency.

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Chang Yang, Peng Zhang, Hui Gao, Jing Zhang

Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)

Challenge:	Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection.
Approach:	They propose a new multi-task learning framework that mines latent intentions and rumor semantic features . they propose to use event-level and intent-level strategies to establish cognitive anchors .
Outcome:	The proposed framework improves the effectiveness of rumor detection and addresses the challenges present in the field.

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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang

LLM-SLM Collaborative Framework of Idiomatic Expression Generation (2026.acl-long)

Challenge:	Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly.
Approach:	They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size .
Outcome:	The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly.

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Hui Gao, Changhao Song, Peng Zhang, Jing Zhang, Chang Yang, Liuxian Ge

GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety (2026.findings-acl)

Challenge:	Existing methods for idiomatic expression generation lack parallel data and manual annotations.
Approach:	They propose an iterative LLM-SLM collaborative framework that replaces human supervision for idiomatic expression data generation.
Outcome:	The proposed framework outperforms DeepSeek-R1 in Chinese Idiom Polishing with a 25.2% improvement in accuracy.

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Changxuan Fan, Xi Yang, Yueyuan Zheng, Bin Zhou, Yuanping Wang, Wenbin Hu, Huihao Jing, Ki Sen Hung, Dazhao Du, Haoran Li, Janet Hui-wen Hsiao, Yangqiu Song

Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)

Challenge:	a survey of older adults shows that many LLMs mishandle elderly-specific contextual risks.
Approach:	They propose a framework to assess elderly-specific contextual risks in LLM interactions . they use a taxonomy to identify 50 fine-grained risk types across mental well-being, financial, medical, toxicity, and privacy domains .
Outcome:	a new framework assesses elderly-specific contextual risks in LLM interactions . it achieves 96.2% and 90.9% unsafe-prompt detection accuracy, respectively .

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Changlin Yang, Siye Liu, Sen Hu, Wangshu Zhang, Teng Xu, Jing Zheng

Natural Language Interfaces

Challenge:	Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa .
Approach:	They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance.
Outcome:	The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes.

Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors (2025.acl-long)

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Senqi Yang, Dongyu Zhang, Jing Ren, Ziqi Xu, Xiuzhen Zhang, Yiliao Song, Hongfei Lin, Feng Xia

Sentiment Analysis

Challenge:	Metaphors are pervasive in communication, making them crucial for natural language processing.
Approach:	They propose a multicultural multimodal metaphor dataset designed for cross-cultural studies of metaphor in Chinese and English.
Outcome:	The proposed model improves metaphor comprehension across cultural backgrounds and cultural domains.

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)

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Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, Liang Pang

Sentiment Analysis

Challenge:	Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences .
Approach:	They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features .
Outcome:	The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information.

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Natural Language Interfaces

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

Identifying Unlearned Data in LLMs via Membership Inference Attacks (2025.emnlp-main)

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Advit Deepak, Megan Mou, Jing Huang, Diyi Yang

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

Challenge:	Existing work evaluates approximate unlearning under a retrieval paradigm, where adversaries attempt to extract residual knowledge given partial information of the unlearning target.
Approach:	They propose a framework to evaluate unlearning membership attacks using member inference techniques to exploit the forget set.
Outcome:	The proposed framework assesses whether unlearning leaves behind detectable artifacts that can be exploited to infer membership in the forget set.

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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang

Text Generation

Challenge:	Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach:	They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome:	The proposed approach outperforms existing methods in 84.33% of the trials.

Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs (2026.acl-long)

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Chen Yang, Ruping Xu, Ruizhe Li, Bin Cao, Jing Fan

HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)

Challenge:	Existing benchmarks for extracting structured procedural knowledge from unstructured business documents are limited by simplistic schemas and shallow logical dependencies.
Approach:	They propose a framework for extracting structured procedural knowledge from unstructured business documents . they propose BREX, a carefully curated benchmark comprising 409 real-world business documents and 2,855 expert-annotated rules .
Outcome:	The proposed framework outperforms standard prompts in rule extraction and execution.

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Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

CCG: Rare-Label Prediction via Neural SEM–Driven Causal Game (2025.findings-emnlp)

Challenge:	Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information.
Approach:	They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner.
Outcome:	The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines.

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Yijia Fan, Jusheng Zhang, Kaitong Cai, Jing Yang, Keze Wang

HS-GC: Holistic Semantic Embedding and Global Contrast for Effective Text Clustering (2024.lrec-main)

Challenge:	Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, distribution shifts, especially in rare label prediction.
Approach:	They propose a Causal Cooperative Game framework that models multi-player cooperative process for multi-label classification.
Outcome:	The proposed framework improves rare label prediction and overall robustness compared to baselines.

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Chen Yang, Bin Cao, Jing Fan

Text Clustering

Challenge:	In this paper, we introduce Holistic Semantic Embedding and Global Contrast (HS-GC) to learn the instance- and cluster-level representations.
Approach:	They propose a novel loss function that exploits different layers of semantic information in a deep neural network to provide a more holistic semantic text representation.
Outcome:	The proposed model outperforms the state-of-the-art model on five text datasets and improves clustering accuracy of 5.9% and 3.2% on the StackOverflow and TREC datasets.

Demystifying Verbatim Memorization in Large Language Models (2024.emnlp-main)

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Jing Huang, Diyi Yang, Christopher Potts

PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)

Challenge:	Existing studies have shown that Large Language Models (LLMs) memorize long sequences verbatim, with serious copyright and privacy implications.
Approach:	They develop a framework to study verbatim memorization in a controlled setting by continuing pre-training from Pythia checkpoints with injected sequences.
Outcome:	The proposed framework creates a control model M () and a treatment model M with injected sequences.

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Pengwei Zhan, Chao Zheng, Jing Yang, Yuxiang Wang, Liming Wang, Yang Wu, Yunjian Zhang

Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

Challenge:	Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario.
Approach:	They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance.
Outcome:	The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time.

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Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

Challenge:	Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach:	They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome:	The proposed approach achieves state-of-the-art on two benchmark datasets.

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding (2026.findings-acl)

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

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Qinglin Zeng, Jusheng Zhang, Jing Yang, Ningyuan Liu, Keze Wang

Multiplex Graph Neural Network for Extractive Text Summarization (2021.emnlp-main)

Challenge:	Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement, but their current decoding paradigms are static and myopic.
Approach:	They propose a Regret-Aware Confidence Calibration framework that aligns decoding decisions with the model’s latent self-correction capabilities.
Outcome:	The proposed framework aligns decoding decisions with model’s latent self-correction capabilities.

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Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, Hanghang Tong

Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)

Challenge:	Existing methods for extractive text summarization do not consider multiple types of inter-sentential relationships, nor model intra-sententential relationships.
Approach:	They propose a novel method to combine different types of relationships among sentences and words to model sentence embedding.
Outcome:	The proposed model is compared with existing methods on CNN/DailyMail benchmark dataset to demonstrate its effectiveness.

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Ruilin Luo, Jiayi Li, Jianghangfan Zhang, Jing Xiao, Yujiu Yang

CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification (2024.eacl-long)

Challenge:	Existing knowledge graphs lack robustness and incompleteness to provide link prediction.
Approach:	They propose to capture prior schema-level interactions related to relations by leveraging entity type information and introduce schema-guided negatives to bolster the efficiency of normal contrastive representation learning.
Outcome:	The proposed method achieves state-of-the-art performance on multiple established metrics across multiple datasets for link prediction.

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Yang Li, Canran Xu, Guodong Long, Tao Shen, Chongyang Tao, Jing Jiang

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)

Challenge:	Existing methods to improve pre-trained language models for many-class classification suffer from verbalizer ambiguity . a significant disparity exists between the pre-training and fine-tuning stages of the model .
Approach:	They propose a method to tune pre-trained language models to a broad spectrum of tasks . they use an instance-dependent soft prefix to complement language verbalizers in many-class classification .
Outcome:	The proposed method outperforms baselines on many-class datasets.

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Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma

Challenge:	Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks .
Approach:	They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience.
Outcome:	The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning.

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang

CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration (2025.acl-long)

Challenge:	Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge.
Approach:	They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types.
Outcome:	The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets.

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Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Ee-Peng Lim

Challenge:	Motivational Interviewing (MI) is a client-centered counseling technique designed to address ambivalence and facilitate behavior change in clients.
Approach:	They propose to use a STAR framework to evoke change talk by using large language models to assess MI skill competency, client’s state inference accuracy, topic exploration proficiency, and overall counseling success.
Outcome:	The proposed agent outperforms several state-of-the-art methods and shows more realistic counselor-like behavior.

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)

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Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong

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

Challenge:	Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability.
Approach:	They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality.
Outcome:	The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings.

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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.

Consistency Rating of Semantic Transparency: an Evaluation Method for Metaphor Competence in Idiom Understanding Tasks (2025.coling-main)

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Hui Gao, Jing Zhang, Peng Zhang, Chang Yang

Persona Prompting as a Lens on LLM Social Reasoning (2026.eacl-long)

Challenge:	Idioms condense complex semantics into fixed phrases, making idiom comprehension a test of metaphor competence.
Approach:	They propose a method to evaluate the metaphor competence of LLMs for the idiom understanding task: the Consistency Rating of Semantic Transparency (CR-ST).
Outcome:	The proposed method assesses the difficulty of understanding idioms through two dimensions: overall semantic transparency and constituent semantic transparency, aiming to gauge LLMs’ mastery of metaphor competence.

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Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov, Evelyn Luise Brinkmann, Vera Schmitt, Nils Feldhus

Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility (2024.lrec-main)

Challenge:	Persona prompting (PP) is increasingly used to steer large language models towards user-specific generation, but its effect on rationales remains underexplored.
Approach:	They examine how LLM-generated rationales vary when conditioned on different demographic personas . they use word-level rationale annotations to measure agreement with human annotations based on PP .
Outcome:	The proposed model improves classification on the most subjective task, but fails to align with real-world demographic counterparts.

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Pengwei Zhan, Jing Yang, He Wang, Chao Zheng, Liming Wang

Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning (2022.findings-naacl)

Challenge:	Neural language models have demonstrated impressive performance but remain vulnerable to word-level adversarial attacks.
Approach:	They propose two standardized search spaces to address the problem of word-level adversarial attacks.
Outcome:	The proposed search spaces improve performance and trade-offs in different scenarios.

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Hongzhan Lin, Jing Ma, Liangliang Chen, Zhiwei Yang, Mingfei Cheng, Chen Guang

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	Existing rumor detection methods are poor at detecting false rumors about breaking news or trending topics due to the lack of training data and prior knowledge.
Approach:	They propose an adversarial contrastive learning framework to detect false rumors by adapting features learned from well-resourced rumor data to that of the low-resource.
Outcome:	The proposed framework improves on two low-resource datasets and shows superior performance . it overcomes restriction of domain and/or language usage and improves robustness .

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Information Retrieval

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

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Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)

Challenge:	Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task.
Approach:	They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent.
Outcome:	The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm.

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Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, Rongsheng Zhang

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval (2026.findings-acl)

Challenge:	Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in the realm of literary creation.
Approach:	They propose a framework for unleashing the creativity of large language models (LLMs) they assign LLMs to different roles involved in real-world scenario, they write .
Outcome:	The proposed framework outperforms baselines in terms of coherence, relevance, interestingness and overall quality on automatically generated screenplays.

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Yifan Song, Xingjian Tao, Zhicheng Yang, Yihong Luo, Jing Tang

Information Retrieval

Challenge:	Existing lightweight approaches to retrieval-augmented generation fail to capture latent semantic connections between disjoint entities.
Approach:	They propose a lightweight RAG framework that constructs a hypergraph capturing both structure and semantic relationships using a hybrid structural-semantic retrieval mechanism.
Outcome:	EHRAG outperforms state-of-the-art methods on four datasets while maintaining zero token consumption.

An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing (2024.acl-long)

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Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, Yang Yang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide spectrum of tasks, but performance and reliability in certain specialized domains still fall short of expectations.
Approach:	They propose a unified generalist framework that facilitates seamless integration of multiple expert LLMs.
Outcome:	The proposed framework outperforms existing multi-LLM collaboration paradigms across six diverse expert domains.

Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)

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Jing Lu, Gustavo Hernandez Abrego, Ji Ma, Jianmo Ni, Yinfei Yang

Information Retrieval

Challenge:	Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space.
Approach:	They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques.
Outcome:	The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages.

Contrastive Learning with Adversarial Examples for Alleviating Pathology of Language Model (2023.acl-long)

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Pengwei Zhan, Jing Yang, Xiao Huang, Chunlei Jing, Jingying Li, Liming Wang

Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models (2025.naacl-long)

Challenge:	Existing interpretation methods fail to obtain faithful attributions on these models, thereby failing to reveal potential flaws and biases.
Approach:	They propose a Contrastive learning regularization method which calibrates the sentence representation of out-of-distribution examples and utilizes adversarial examples to introduce direction information in regularization.
Outcome:	The proposed method alleviates the model pathology while impacting generalization ability on in-distribution examples and thus helps interpretation methods obtain more faithful results.

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Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory.
Approach:	They propose a propositional logic prompting method which generates expanded logical information descriptions and utilizes them as an additional augmentation to original contexts.
Outcome:	Extensive experiments show that Logic-of-Thought boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks.

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Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang

Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)

Challenge:	Existing methods for text data augmentation are limited to simple tasks and weak baselines.
Approach:	They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data.
Outcome:	The proposed method improves many tasks while not negatively affecting the others.

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Yue Gao, Jing Zhao, Shiliang Sun, Xiaosong Qiao, Tengfei Song, Hao Yang

A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)

Challenge:	Multimodal machine translation (MMT) models focus on intermodal interactions, but focus on simple interactions between nouns and entities in image, overlooking global semantic alignment.
Approach:	They propose a Text-Image In-depth Questioning method to deepen interactions and optimize translations by utilizing visual data to capture global semantic alignment.
Outcome:	The proposed method achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark.

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Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, Zhilin Yang