Papers by Lin Yao

DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models (2026.acl-long)

Challenge:	Existing methods for event argument extraction rely on a single prompt . existing methods ignore complex structural and dynamic interdependencies between event arguments .
Approach:	They propose a multi-prompt learning framework that generates event arguments via multi-perspective prompts and ontology steering.
Outcome:	The proposed framework captures interrelationships between arguments and ontology steering . it uses multiple unfilled prompts for each sentence to generate event arguments .

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Xinwei Li, Li Lin, Hui Jiao, Li Yao, Tien-Tsin Wong, Hanqian Wu

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

Challenge:	Existing decoding-based approaches do not explicitly decouple visual evidence from mixed vision–language representations.
Approach:	They propose to decouple visual evidence from mixed vision–language representations by dynamically identifying layers enriched with visual information and performing intra-layer decoupling to extract aggregated visual evidence.
Outcome:	Experiments show that DiVE achieves state-of-the-art performance on multiple benchmarks.

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Jiasheng Zhang, Xikai Liu, Xinyi Lai, Yan Gao, Shusen Wang, Yao Hu, Yiqing Lin

Low-Resource NLP

Challenge:	Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP).
Approach:	They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence.
Outcome:	The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms.

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

T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.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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Han Lin, Xiu Tang, Huan Li, Wenxue Cao, Sai Wu, Chang Yao, Lidan Shou, Gang Chen

Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data (D18-1)

Challenge:	Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data.
Approach:	They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies.
Outcome:	The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks.

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Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, Chin-Yew Lin

Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)

Challenge:	Existing work on grounded language learning does not capture the semantics of correspondences between structured world state representations and texts.
Approach:	They propose to learn explicit latent semantic annotations from paired structured tables and texts . they use an adapted semi-hidden Markov model to impose a soft constraint to further improve performance .
Outcome:	The proposed framework improves on a semi-hidden Markov model and extracts templates for language generation.

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Qingsong Wang, Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, Jingyuan Chen

Information Retrieval

Challenge:	Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment.
Approach:	They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition.
Outcome:	The proposed framework aligns knowledge complexity and presentation style with user cognition.

Natural Language Generation for Effective Knowledge Distillation (D19-61)

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Raphael Tang, Yao Lu, Jimmy Lin

Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)

Challenge:	Knowledge distillation can transfer knowledge from deep language representation models to shallow word embedding-based neural networks.
Approach:	They propose to build an unlabeled transfer dataset to enable effective knowledge transfer . they hypothesize that this principled, general approach outperforms rule-based techniques .
Outcome:	The proposed method outperforms OpenAI GPT on four datasets in sentiment classification, sentence similarity, and linguistic acceptability.

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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang

CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)

Challenge:	Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks.
Approach:	They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem.
Outcome:	The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings.

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Yisen Li, Lingfeng Yang, Wenxuan Shen, Pan Zhou, Yao Wan, Weiwei Lin, Dongping Chen

Machine Translation

Challenge:	Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields.
Approach:	They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity.
Outcome:	The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning.

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)

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Ruikang Liu, Haoli Bai, Haokun Lin, Yuening Li, Han Gao, Zhengzhuo Xu, Lu Hou, Jun Yao, Chun Yuan

TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)

Challenge:	Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation.
Approach:	They propose to generate the KV cache of pivot tokens losslessly from the full-precision model.
Outcome:	The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead.

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Dingyu Yao, Bowen Shen, Zheng Lin, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

Challenge:	Existing work mitigates memory overhead by offloading or compressing the Key-Value cache.
Approach:	They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method.
Outcome:	The proposed method outperforms the state-of-the-art in long-context evaluations.

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

Responsible & Trustworthy NLP

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

Using Intermediate Representations to Solve Math Word Problems (P18-1)

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Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin

Numerical Reasoning

Challenge:	Existing approaches to solving math word problems do not include higher-order operations that cannot be explicitly represented in equations.
Approach:	They propose an iterative labeling framework that generates intermediate forms and executes them to obtain the final answers.
Outcome:	The proposed model outperforms existing models in solving math word problems.

SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)

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Yao Zhang, Chenyang Lin, Shijie Tang, Haokun Chen, Shijie Zhou, Yunpu Ma, Volker Tresp

Challenge:	Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability.
Approach:	They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination .
Outcome:	The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark.

Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)

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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Open Hierarchical Relation Extraction (2021.naacl-main)

Challenge:	Current diffusion models do not cover recent models, thus we curate three test sets for evaluation.
Approach:	They propose a human-calibrated measure of variability in a set of images bootstrapped from existing image-pair perceptual distances.
Outcome:	The proposed model outperforms nine baselines by 18 points in accuracy and matches graded human judgements 78% of the time.

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Kai Zhang, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)

Challenge:	Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency.
Approach:	They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations.
Outcome:	The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion.

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Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin

Text Generation

Challenge:	Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data.
Approach:	They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data.
Outcome:	The proposed framework improves the fidelity of the generated texts to the input structured data.

Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)

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Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Audio-Aware Large Language Models as Judges for Speaking Styles (2025.findings-emnlp)

Challenge:	Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive.
Approach:	They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data.
Outcome:	Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods.

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Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang

CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)

Challenge:	Audio-aware large language models (ALLMs) can understand textual and non-textual information in the audio input.
Approach:	They use audio-aware large language models (ALLMs) to evaluate the speaking styles of SLMs on two tasks: voice style instruction following and role-playing.
Outcome:	The proposed models can understand the textual and non-textual information in the audio input and can be used as a judge to assess the speaking styles of SLMs.

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Yujie Shao, Xinrong Yao, Xingwei Qu, Chenghua Lin, Shi Wang, Wenhao Huang, Ge Zhang, Jie Fu

Challenge:	Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication.
Approach:	They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations .
Outcome:	The proposed corpus generates metaphors that resonate more with real-world intuition.

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)

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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

Improving Factual Consistency of News Summarization by Contrastive Preference Optimization (2024.findings-emnlp)

Challenge:	Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor.
Approach:	They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level.
Outcome:	The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors.

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Huawen Feng, Yan Fan, Xiong Liu, Ting-En Lin, Zekun Yao, Yuchuan Wu, Fei Huang, Yongbin Li, Qianli Ma

Text Generation

Challenge:	Recent advances in news summarization have created problems with “hallucinations” that are factually inconsistent with the source text.
Approach:	They propose to disentangle LLMs’ propensities to generate faithful and fake content by adopting a probing-based specific training method to improve their capacity of distinguishing two types of propensity.
Outcome:	The proposed method disentangles LLMs’ propensities to generate faithful and fake content and improves their ability to distinguish between two types of propensity.

Why Do Emotions Change? Appraisal-Guided Reasoning for Emotion–Cause Triplet Extraction in Conversations (2026.acl-long)

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Qiao Liang, Ying Shen, Yao Liu, Tiantian Chen, Lin Zhang

GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)

Challenge:	Existing methods for multi-turn, multi-speaker multimodal affect understanding are difficult to maintain conversation-level consistency under within-speaks' emotion shifts.
Approach:	They propose a framework that combines appraisal-guided structured generation with graph-structured reinforcement learning to extract triplets from multi-turn multimodal conversations.
Outcome:	The proposed framework outperforms baselines on public MECTEC benchmarks and improves structure-aware metrics on emotion shift coherence and core events.

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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun

Visual Data in NLP

Challenge:	Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction.
Approach:	They propose a series of datasets for training visual-based GUI agents using general VLMs.
Outcome:	The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks.

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

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Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin

N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator (2026.acl-long)

Challenge:	Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation.
Approach:	They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs.
Outcome:	The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time.

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Zheyu Lin, Jirui Yang, Yukui Qiu, Yubing Bao, Hengqi Guo, Yao Guan

Data2Text Studio: Automated Text Generation from Structured Data (D18-2)

Challenge:	Evaluating the safety robustness of LLMs is critical for their deployment.
Approach:	They propose to use latent representations to characterize hidden layer dynamics by analyzing the APT of latent models and introducing the JSS metric.
Outcome:	The proposed method exploits the APT (Angular-Probabilistic Trajectory) of latent representations and introduces the JSS (Jensen-Shannon Separability) metric.

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Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin

Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)

Challenge:	Data2Text Studio is a platform for automated text generation from structured data.
Approach:	They conduct experiments on RotoWire datasets for template extraction and text generation . they find that the Semi-HMMs model improves interactivity and interpretability .
Outcome:	The proposed model improves on template extraction and text generation tasks on RotoWire datasets.

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Tianyu Liu, Jin-Ge Yao, Chin-Yew Lin

Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)

Challenge:	Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.
Approach:	They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities.
Outcome:	The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features.

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Xuefei Li, Huiwei Zhou, Weihong Yao, Wenchu Li, Yingyu Lin, Lei Du

Knowledge Representation

Challenge:	Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive.
Approach:	They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns.
Outcome:	The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%.

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.

Half-S: Halving the Scale for Near-Lossless 4-Bit LLM Training (2026.findings-acl)

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Jinyang Du, Ruihao Gong, Linghan Ai, Zining Wang, Yunke Peng, Yao Wang, Lei Yan, null Wxuefei, Yaoyuan Wang, Jinyang Guo, Dahua Lin, Xianglong Liu

Green & Sustainable NLP

Challenge:	Existing 4-bit training pipelines rely on max-scaling, which causes representation collapse . despite this, there are limitations in the accuracy of 4-bit LLM training .
Approach:	They propose a scaling strategy that uses half-scaling as a hardware-friendly default . they propose fp4 support that allows for a faster scaling of large language models .
Outcome:	The proposed scaling strategy narrows the gap between theoretical optimum and BF16 while maintaining the efficiency benefits of 4-bit training.

Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)

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Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin

A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)

Challenge:	Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents.
Approach:	They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks.
Outcome:	The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark.

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Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong Pan, Chin-Yew Lin

Reasoning

Challenge:	Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text.
Approach:	They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text.
Outcome:	Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator.

Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)

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Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng

R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)

Challenge:	Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways.
Approach:	They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods.
Outcome:	The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets.

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Kaijie Chen, Zihao Lin, Zhiyang Xu, Ying Shen, Yuguang Yao, Joy Rimchala, Jiaxin Zhang, Lifu Huang

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

Challenge:	Reasoning is a fundamental capability underpinning text-to-image (T2I) generation.
Approach:	They propose a benchmark to rigorously assess reasoning-driven T2I generation.
Outcome:	Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation .

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL (2025.findings-acl)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu

Linguistics & Cognitive NLP

Challenge:	Weak-to-strong generalization is a promising approach to guide stronger systems, but its effectiveness is constrained by the inherent imperfections of weak model supervision.
Approach:	They propose a theoretically grounded approach that replaces forward KL divergence with reverse KL, which prioritizes high-confidence predictions.
Outcome:	The proposed approach replaces forward KL divergence with reverse KL, reducing the influence of unreliable weak supervision.

Dependency Parsing via Sequence Generation (2022.findings-emnlp)

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Boda Lin, Zijun Yao, Jiaxin Shi, Shulin Cao, Binghao Tang, Si Li, Yong Luo, Juanzi Li, Lei Hou

Syntactic Text Processing

Challenge:	Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method.
Approach:	They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures.
Outcome:	The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16.

HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization (2026.findings-acl)

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Hanyun Jiang, Peisen Yao, Kaiyue Li, Tingting Lin, Chengpeng Wang, Kui Ren

VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization (2026.acl-long)

Challenge:	Existing methods to optimize source code rely on invasive transformations that can introduce semantic errors and miss fine-grained compiler-level optimization opportunities.
Approach:	They propose a method that bridges LLM-based reasoning with traditional compilers by synthesizing compiler hints.
Outcome:	HintPilot achieves 6.88x speedup over -Ofast while preserving program correctness.

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Dingyu Yao, Chenxu Yang, Zhengyang Tong, Zheng Lin, Wei Liu, Jian Luan, Weiping Wang

Green & Sustainable NLP

Challenge:	Existing quantization methods for large language models suffer performance degradation at ultra-low bit-widths due to key cache outliers.
Approach:	They propose a vector quantization method that suppresses outliers in the key cache and reduces memory access overhead.
Outcome:	The proposed method outperforms baseline quantization methods across long-context understanding and mathematical reasoning tasks while minimizing memory access overhead.

MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)

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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao

Responsible & Trustworthy NLP

Challenge:	Existing models that generate generic aspects do not provide personalized informative recommendations.
Approach:	They propose a model that integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms.
Outcome:	The proposed model outperforms baseline model on restaurant review datasets in the restaurant domain.

ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)

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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen

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

Challenge:	Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models.
Approach:	They propose a dataset that provides rigorous evaluation of multi-hop tool use.
Outcome:	The proposed model achieves 49.04% accuracy across five model families.

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

Text Generation

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.

Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)

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Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao

Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)

Challenge:	Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI.
Approach:	They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input.
Outcome:	The proposed models perform better than models fine-tuned for BERT, but the results are in general negative.

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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low

CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)

Challenge:	a paper proposes a data-centric perspective of AI research, focusing on large language models.
Approach:	They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer .
Outcome:	The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods .

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Daoguang Zan, Ailun Yu, Wei Liu, Bo Shen, Shaoxin Lin, Yongshun Gong, Yafen Yao, Yan Liu, Bei Guan, Weihua Luo, Yongji Wang, Qianxiang Wang, Lizhen Cui

Challenge:	Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios.
Approach:	They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios.
Outcome:	The proposed model can train a multilingual multitasking model using less data and training data.

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)

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Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, Lingyao Li

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP).
Approach:	They investigate the vulnerability of Large Language Models to hidden adversarial prompts . they evaluate two critical attack scenarios: malicious content relay and sensitive data leakage .
Outcome:	The proposed extension could introduce new security vulnerabilities.

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun