Papers by Su Yang

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models (2026.acl-long)

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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Zhiwen Ruan, Yichao Du, Jianjie Zheng, Longyue Wang, Yun Chen, Peng Li, Jinsong Su, Yang Liu, Guanhua Chen

Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)

Challenge:	Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation.
Approach:	They propose a framework that incorporates instruction-level guidance into task adaptation.
Outcome:	The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior.

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Xin Liu, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Min Zhang, Haiying Zhang, Jinsong Su

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)

Challenge:	Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks .
Approach:	They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token .
Outcome:	The proposed approach allows for more efficient and better performed NLG models.

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Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, Kai Yu

Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)

Challenge:	Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances.
Approach:	They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances.
Outcome:	The proposed framework is effective and compatible with supervised training.

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Rui Li, Wenlin Zhao, Cheng Yang, Sen Su

Information Extraction & Text Mining

Challenge:	Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies .
Approach:	They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features.
Outcome:	The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets.

ToolRM: Towards Agentic Tool-Use Reward Modeling (2026.findings-acl)

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Renhao Li, Jianhong Tu, Yang Su, Yantao Liu, Fei Huang, Hamid Alinejad-Rokny, Derek F. Wong, Junyang Lin, Min Yang

Challenge:	lack of reliable reward models for tool-use tasks has limited progress toward agentic AI . recent advances in agentic artificial intelligence are driven by tool-using capabilities of large language models.
Approach:	They propose a pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling to build lightweight reward models.
Outcome:	The proposed model outperforms existing models on tool calling tasks with higher accuracy.

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

Sentiment Analysis

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)

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Sujin Chen, Kang Wang, Zixuan Zhou, Xiangyu Duan, Wanqun Zhang, Hao Yang, Jinsong Su, Min Zhang

Explainability & Interpretability in NLP

Challenge:	Current fine-grained error analyses do not ground the errors to the reasons why the annotated text spans are erroneous.
Approach:	They use a bi-directional grounding scheme to ground erroneous text in two directions . if the error spans of both directions are consistent, the explanation is valid .
Outcome:	The proposed grounding process improves translation error detection significantly.

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)

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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao

Dialogue Systems & Conversational Agents

Challenge:	Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue.
Approach:	They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions.
Outcome:	The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses.

CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)

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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang

Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)

Challenge:	End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data.
Approach:	They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances.
Outcome:	The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets.

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Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Understanding How Value Neurons Shape the Generation of Specified Values in LLMs (2025.findings-emnlp)

Challenge:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework.
Approach:	They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer.
Outcome:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework.

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Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, Di Wang

Explainability & Interpretability in NLP

Challenge:	Current approaches to interpret value representations are limited by superficial judgments over mechanistic analysis.
Approach:	They propose a mechanistic interpretability framework that uses the Schwartz Values Survey to interpret value . they use a dataset that operationalizes four dimensions of universal value through behavioral contexts .
Outcome:	The proposed method bridges psychological value frameworks with neuron analysis in large language models.

TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)

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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Green & Sustainable NLP

Challenge:	Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate .
Approach:	They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity .
Outcome:	The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds.

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024.findings-acl)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Hao Yue, Shaopeng Lai, Chengyi Yang, Liang Zhang, Junfeng Yao, Jinsong Su

Information Extraction & Text Mining

Challenge:	Existing studies on relation extraction ignore non-bridge entities, leading to bias during inference.
Approach:	They propose a graph-based cross-document Relation Extraction model with non-bridge entity enhancement and prediction debiasing that integrates non-cross entities with target entities and bridge entities.
Outcome:	The proposed model outperforms baseline models on open and closed datasets.

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

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

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

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

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)

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Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems (2022.findings-naacl)

Challenge:	Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document .
Approach:	They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document.
Outcome:	The proposed model over-estimates tokens and makes it well-calibrated on common datasets.

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Charlie Snell, Sherry Yang, Justin Fu, Yi Su, Sergey Levine

Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)

Challenge:	Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control.
Approach:	They propose a method to fine-tune language models in a goal-aware way . they evaluate a flight-booking method with a context-assisted language model .
Outcome:	The proposed method outperforms the state-of-the-art method on a flight-booking task by 7% in terms of task success.

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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su

Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)

Challenge:	Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches .
Approach:	They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap .
Outcome:	The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets .

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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)

Challenge:	Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined.
Approach:	They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process.
Outcome:	The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness.

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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

Natural Language Interfaces

Challenge:	Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size.
Approach:	They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences.
Outcome:	The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size.

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

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

Constituency Lattice Encoding for Aspect Term Extraction (2020.coling-main)

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

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Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su

Sentiment Analysis

Challenge:	a challenge for aspect term extraction is to extract phrase-level aspect terms . a constituency lattice structure is constructed using the span annotations of constituents of a sentence .
Approach:	They propose to incorporate the span annotations of constituents of a sentence to leverage syntactic information in neural network models.
Outcome:	The proposed model outperforms existing models on two benchmark datasets.

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)

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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)

Challenge:	None. None.. None!
Approach:	None. None.. None!
Outcome:	None. None. No. :

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Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao

Learning to Answer Psychological Questionnaire for Personality Detection (2021.findings-emnlp)

Challenge:	Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model.
Approach:	They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains.
Outcome:	The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks.

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Feifan Yang, Tao Yang, Xiaojun Quan, Qinliang Su

Representation Learning

Challenge:	Existing text-based personality detection research relies on data-driven approaches to implicitly capture personality cues in online posts lacking the guidance of psychological knowledge.
Approach:	They propose a model to capture key information in texts and a questionnaire to help the user to make a personality assessment.
Outcome:	The proposed model captures key information in texts and a questionnaire and can be used to improve personality prediction.

Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)

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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang

Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities.
Approach:	They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning.
Outcome:	The proposed framework enhances evaluation and facilitates removal of harmful abilities.

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Yuxia Wang, Minghan Wang, Yimeng Chen, Shimin Tao, Jiaxin Guo, Chang Su, Min Zhang, Hao Yang

Reasoning

Challenge:	Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions.
Approach:	They propose to capture the human disagreement distribution from the perspective of model calibration.
Outcome:	The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy.

News2vec: News Network Embedding with Subnode Information (D19-1)

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Ye Ma, Lu Zong, Yikang Yang, Jionglong Su

Representation Learning

Challenge:	Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news.
Approach:	They propose a model that integrates news features and inter-textual knowledge into a dense vector representation.
Outcome:	The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks .

Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)

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Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su

Natural Language Interfaces

Challenge:	Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets.
Approach:	They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments .
Outcome:	The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset.

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)

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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun

AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization via Multi-LLMs (2025.findings-naacl)

Challenge:	Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters.
Approach:	They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters.
Outcome:	The proposed method is compatible with a tunable module and tested on 11 NLP tasks.

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Jiawei Chen, Xiao Yang, Zhengwei Fang, Yu Tian, Yinpeng Dong, Zhaoxia Yin, Hang Su

Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)

Challenge:	Existing jailbreak research exhibits limitations in universality, validity, and efficiency . Existing methods for jailbreaking LLMs have limited validity and effectiveness .
Approach:	They propose a black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts.
Outcome:	The proposed method efficiently identifies security vulnerabilities across various LLMs, achieving an average success rate of over 80% with fewer than 10 queries.

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Xiaoyu Shen, Yang Zhao, Hui Su, Dietrich Klakow

Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)

Challenge:	Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment.
Approach:	They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them.
Outcome:	The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations.

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Yilun Liu, Shimin Tao, Chang Su, Min Zhang, Yanqing Zhao, Hao Yang

Machine Translation

Challenge:	Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples .
Approach:	They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty.
Outcome:	The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks.

Failure makes the agent stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions (2026.findings-acl)

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Junhao Su, Yuanliang Wan, Junwei Yang, Hengyu Shi, Tianyang Han, Yurui Qiu, Junfeng Luo

DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents (2026.findings-acl)

Challenge:	Existing approaches to self-reflection rely on heuristic prompting or unidirectional reasoning traces.
Approach:	They propose a structured reflection method that transforms the "from error to repair" process into a first-class, controllable, and trainable action.
Outcome:	The proposed method improves multi-turn tool-call success rates and error recovery while reducing redundant calls.

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Kai Shi, Jun Yang, Ni Yang, Binqiang Pan, Qingsong Xie, null Zhangchao, Zhenyu Yang, Tianhuang Su, Haonan Lu

A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (2020.acl-main)

Challenge:	Mobile Phone Agents (MPAs) have attracted huge attention due to their practicability in a multitude of scenarios.
Approach:	They propose a data mixture optimization solution that extrapolates optimal data mixtures from a trainable network.
Outcome:	The proposed model outperforms existing methods on open-source benchmarks and on open source benchmarks.

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Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, Jiebo Luo

Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)

Challenge:	Existing multi-modal neural machine translation models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities.
Approach:	They propose a graph-based multi-modal fusion encoder that exploits fine-grained semantic correspondences between different modalities.
Outcome:	The proposed encoder significantly extends the conventional text-based translation by taking images as additional inputs.

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Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu Sun

Sentiment Analysis

Challenge:	Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation .
Approach:	They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently .
Outcome:	The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets.

Adaptive Gating in Mixture-of-Experts based Language Models (2023.emnlp-main)

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Jiamin Li, Qiang Su, Yitao Yang, Yimin Jiang, Cong Wang, Hong Xu

Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning (2026.findings-acl)

Challenge:	Existing models employ a fixed gating network where each token is computed by the same number of experts.
Approach:	They propose a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution.
Outcome:	The proposed model reduces training time and inference quality while maintaining sparsity while maintaining inference accuracy.

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Tiehua Mei, Minxuan Lv, Leiyu Pan, Zhenpeng Su, Hongru Hou, Hengrui Chen, Ao Xu, Deqing Yang

Reasoning

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance.
Approach:	They propose a method that reweights rewards by a factor approximately proportional to Evidence Gain and assigns higher weights to high-quality traces without requiring costly computation.
Outcome:	Experiments on mathematical reasoning benchmarks show that Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally.

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen

Information Retrieval

Challenge:	Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach:	They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome:	The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)

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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su

Machine Translation

Challenge:	Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains.
Approach:	They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore.
Outcome:	The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore.

Red Teaming Large Reasoning Models (2026.acl-long)

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Jiawei Chen, Yang Yang, Chao Yu, Yu Tian, Zhi Cao, Xue Yang, Linghao Li, Hang Su, Zhaoxia Yin

AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)

Challenge:	Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, but they introduce safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies.
Approach:	They propose a unified benchmark to assess the trustworthiness of Large Reasoning Models.
Outcome:	The proposed benchmark evaluates truthfulness, safety and efficiency on 26 models.

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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian

Challenge:	Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs).
Approach:	They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say .
Outcome:	The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels.

Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)

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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu

Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)

Challenge:	Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework .
Approach:	They propose a training-free inference framework that simulates a metacognitive self-correction process.
Outcome:	The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE.

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Yan Gao, Zhiwei Cao, Zhongjian Miao, Baosong Yang, Shiyu Liu, Min Zhang, Jinsong Su

Information Retrieval

Challenge:	Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead.
Approach:	They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model.
Outcome:	The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets.

Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)

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Junyang Lin, Qi Su, Pengcheng Yang, Shuming Ma, Xu Sun

Information Extraction & Text Mining

Challenge:	a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text.
Approach:	They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit.
Outcome:	The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels.

Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)

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Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su

Multilinguality

Challenge:	Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity .
Approach:	They propose a multi-candidate optimization framework for diverse NMT to deal with this defect.
Outcome:	The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality.

MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring (2026.acl-long)

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Tengchao Yang, Sichen Guo, Mengzhao Jia, Jiaming Su, Yuanyang Liu, Zhihan Zhang, Meng Jiang

Numerical Reasoning

Challenge:	Existing benchmarks for AI math tutoring largely overlook these skills.
Approach:	They evaluate 12 leading multimodal large language models and find clear performance gaps between them.
Outcome:	The proposed benchmarks show that they can solve 770 problems and provide diagnostics and guidance to students step by step.

Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space (2026.findings-acl)

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Xiang Zhang, Kun Wei, Xu Yang, Jiahua Li, Su Yan, Cheng Deng

Challenge:	Existing methods to remove undesirable data from Large Language Models suffer from cumulative catastrophic utility loss under continuous unlearning requests.
Approach:	They propose a method that leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process.
Outcome:	The proposed method achieves SOTA performance without a retained dataset.

Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026.findings-acl)

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He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Large language models with billions of parameters are often over-provisioned . smaller models exhibit lower robustness under extreme low-bit quantization .
Approach:	They propose a hardware-native, metric-driven post-training quantization framework that keeps uniform bit-width within each layer while mixing precision across layers.
Outcome:	LieQ reduces large accuracy gap observed for large language models with billions of parameters while preserving standard multiplication kernels.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

Applying Contrastive Learning to Code Vulnerability Type Classification (2024.emnlp-main)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Chen Ji, Su Yang, Hongyu Sun, Yuqing Zhang

Co-Evolving LLMs and Embedding Models via Density-Guided Preference Optimization for Text Clustering (2025.emnlp-main)

Challenge:	Recent approaches to classification of vulnerabilities ignore their relationships and treat each class in isolation, resulting in non-scalable code vector representations.
Approach:	They propose a hierarchical contrastive learning framework to bring vector representations of related CWEs closer together and use max-pooling to enable the model to handle longer vulnerability code inputs.
Outcome:	The proposed framework outperforms state-of-the-art methods by 2.97%-17.90% on accuracy and 0.98%-22.27% on weighted-F1 with even better performance on higher-quality datasets.

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Zetong Li, Qinliang Su, Minhua Huang, Yin Yang

DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints (2026.acl-long)

Challenge:	Existing methods for text clustering use static pseudo-oracles, i.e., unidirectionally querying them for similarity assessment or data augmentation.
Approach:	They propose a training framework that enables bidirectional refinement between LLMs and embedding models by using task-aware prompts to guide the LLM in generating interpretations for the input texts.
Outcome:	Experiments on 14 benchmark datasets across 5 tasks demonstrate the effectiveness of the proposed training framework.

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Yinger Zhang, Shutong Jiang, Renhao Li, Jianhong Tu, Yang Su, Lianghao Deng, Xudong Guo, ChenXu Lv, Junyang Lin

Reasoning

Challenge:	Existing LLM planning benchmarks emphasize local, step-level reasoning rather than global constrained optimization.
Approach:	They propose a benchmark for practical long-horizon agent planning that uses local constrained reasoning and global constrained optimization.
Outcome:	The proposed benchmarks show that even frontier agentic LLMs struggle with these problems.

Contextual Domain Classification with Temporal Representations (2021.naacl-industry)

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Tzu-Hsiang Lin, Yipeng Shi, Chentao Ye, Yang Fan, Weitong Ruan, Emre Barut, Wael Hamza, Chengwei Su

Information Retrieval

Challenge:	Existing studies that incorporate context in SLU have focused on domains where context is limited to a few minutes.
Approach:	They propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup.
Outcome:	The proposed model reduces 13.04% of classification errors compared to baseline . previous studies have focused on domains where context is limited to a few minutes .

Personalized Question Answering with User Profile Generation and Compression (2025.findings-emnlp)

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Hang Su, Yun Yang, Tianyang Liu, Xin Liu, Peng Pu, Xuesong Lu

Natural Language Interfaces

Challenge:	Large language models are prone to providing “midguy” answers regardless of users’ knowledge background, thereby failing to meet each user’s personalized needs.
Approach:	They propose to generate personalized answers with LLMs based on users’ past question-answering records.
Outcome:	The proposed method generates personalized answers based on user's past question-answering records.

Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music (2026.acl-long)

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Hongju Su, Ke Li, Lan Yang, Honggang Zhang, Yi-Zhe Song

Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)

Challenge:	Existing symbolic music generation models represent musical notes as a sequence of attribute tokens with fixed unidirectional dependencies.
Approach:	They propose a symbolic music generation framework that adopts a autoregressive and a discrete diffusion architectures for note attributes.
Outcome:	The proposed framework improves state-of-the-art models across objective and subjective metrics.

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Yu Su, Diyi Yang, Shunyu Yao, Tao Yu

Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)

Challenge:	Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments.
Approach:	They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics.
Outcome:	The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas.

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Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu

Dialogue Systems & Conversational Agents

Challenge:	In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved.
Approach:	They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated .
Outcome:	The proposed approach outperforms the baseline model on multiple domain evaluations.

SeedBench: A Multi-task Benchmark for Evaluating Large Language Models in Seed Science (2025.acl-long)

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Jie Ying, Zihong Chen, Zhefan Wang, Wanli Jiang, Chenyang Wang, Zhonghang Yuan, Haoyang Su, Huanjun Kong, Fan Yang, Nanqing Dong

GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning (2026.findings-acl)

Challenge:	Seed science is essential for modern agriculture, but its application in seed science remains limited due to a shortage of experts and limited availability of online resources.
Approach:	They evaluate 26 leading large language models and compare them against a set of benchmarks . they find that there is a gap between the power of LLMs and real-world seed science problems .
Outcome:	The new seed benchmark highlights the gap between the power of large language models and real-world seed science problems.

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Kun Wang, Li Yiming, Mingcheng Qu, Aqiang Zhang, Guang Yang, Tonghua Su

Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning (2024.emnlp-main)

Challenge:	Existing methods for procedural planning over-rely on visual inputs and lack structured semantic information.
Approach:	They propose a vision–language framework for multimodal procedural planning that exploits implicit spatial relations and deep semantics encoded in object attributes.
Outcome:	The proposed framework outperforms existing methods in terms of execution success rate, LCS, and planning correctness.

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Shengguang Wu, Shusheng Yang, Zhenglun Chen, Qi Su

A Self-Denoising Model for Robust Few-Shot Relation Extraction (2025.acl-long)

Challenge:	Existing methods to assess social-pragmatic inference in large language models are inadequacy, and preferential tuning is the best approach.
Approach:	They propose to use free-form models' responses as a measure to assess social-pragmatic reasoning and advocate for preference optimization over supervised finetuning (SFT).
Outcome:	The proposed model outperforms supervised finetuning (SFT) and offers a near-free launch in pragmatic abilities without compromising general capabilities.

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Liang Zhang, Yang Zhang, Ziyao Lu, Fandong Meng, Jie Zhou, Jinsong Su

CLLE: A Benchmark for Continual Language Learning Evaluation in Multilingual Machine Translation (2022.findings-emnlp)

Challenge:	Existing studies assume that the support set contains only accurately labeled instances, but this assumption is often unrealistic.
Approach:	They propose a self-denoising model for FSRE which can automatically correct noisy labels of support instances.
Outcome:	The proposed model outperforms all baselines on two public datasets showing that it can correct mislabeled support instances.

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Han Zhang, Sheng Zhang, Yang Xiang, Bin Liang, Jinsong Su, Zhongjian Miao, Hui Wang, Ruifeng Xu

Multilinguality

Challenge:	Existing benchmarks for Continual Language Learning (CLL) are limited due to the complexity of the task and the lack of unified benchmarks.
Approach:	They propose a Continual Language Learning Evaluation benchmark CLLE in multilingual translation.
Outcome:	The proposed method is effective when compared with other strong benchmarks.

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Programming Languages in NLP

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

Specialist or Generalist? Instruction Tuning for Specific NLP Tasks (2023.emnlp-main)

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Chufan Shi, Yixuan Su, Cheng Yang, Yujiu Yang, Deng Cai

The Fall of ROME: Understanding the Collapse of LLMs in Model Editing (2024.findings-emnlp)

Challenge:	Recent studies have shown that instruction tuning can be a data-efficient method for transforming large language models into generalist models, but their performance lags behind specialist models trained exclusively for specific tasks.
Approach:	They propose to incorporate broadcoverage generalist instruction tuning into large language models to build a specialist model by incorporating task specificity and skill requirements.
Outcome:	The proposed method improves model performance when task coverage is broad and when training data is limited.

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Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Du Su, Dawei Yin, Huawei Shen

BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026.acl-long)

Challenge:	Recent studies have found that model editing methods can cause large language models to collapse with just a single edit.
Approach:	They propose a method that uses prefixed keys and adds prefixes during testing to prevent model collapse.
Outcome:	The proposed method prevents model collapse while maintaining effectiveness, the authors show . Rank-One Model Editing (ROME) has been found to cause model collapse with just a single edit .

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Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng

Towards User-Driven Neural Machine Translation (2021.acl-long)

Challenge:	Post-trained LLMs typically compromise reliability with severe overconfidence, resulting in inaccurate responses.
Approach:	They propose a solution that feeds PoLLMs into the base LLM to get confidence.
Outcome:	The proposed solution reduces expected calibration error (ECE) by 42.90% compared to the best unsupervised baselines.

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Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, Jinsong Su

Multimodal Context Carryover (2022.emnlp-industry)

Challenge:	a good translation should implicitly mirror user traits rather than translate the original content semantically.
Approach:	They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion .
Outcome:	The proposed framework can capture user traits from historical inputs under zero-shot learning fashion.

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Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri

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

Challenge:	Existing voice-only dialogue systems lack multimodality support, which can lead to costly system redesigns.
Approach:	They propose to augment existing voice-only dialogue systems with additional multimodal components to facilitate quick delivery of visual modality support with minimal changes.
Outcome:	The proposed framework improves visual modality support with minimal changes on an in-house multi-modal visual navigation data set.

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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.

Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)

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Suhang Wu, Jialong Tang, Chengyi Yang, Pei Zhang, Baosong Yang, Junhui Li, Junfeng Yao, Min Zhang, Jinsong Su

Multilinguality

Challenge:	Existing methods for terminology translation struggle with interference from irrelevant noise.
Approach:	They propose a Locate-and-Focus method that locates terminologies within utterances to construct translation knowledge by minimizing irrelevant information for ST models.
Outcome:	The proposed method locates terminologies within utterances and enhances the success rate of terminology translation while maintaining robust general translation performance.

Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings (2025.findings-acl)

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Yuanhe Zhang, Zhenhong Zhou, Wei Zhang, Xinyue Wang, Xiaojun Jia, Yang Liu, Sen Su

Robustness in NLP

Challenge:	Existing studies on white-box attacks focus on black-box LLMs, leaving black- box scenarios underexplored.
Approach:	They propose an automated algorithm designed for black-box LLMs that constructs the DoS Attack Tree and expands the node coverage to achieve effectiveness under black- box conditions.
Outcome:	The proposed algorithm can be used to build a DoS Attack Tree and expand the node coverage to achieve effectiveness under black-box conditions.

Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)

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Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, Yunxin Liu