Papers by Sheng Zhang

OptiCo: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning (2026.acl-long)

Challenge:	Lack of large-scale terminology definition dataset hinders definition generation . lack of precise terminology definitions poses great challenges in scientific communication .
Approach:	They propose a large-scale terminology definition dataset Graphine that exploits the graph structure of terminologies to generate graph-aware text generation models.
Outcome:	The proposed model outperforms existing models by exploiting graph structure of terminologies.

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Sheng Chen, Tang Zhe, Weixing Zhang, Fei Yang, Yuanyuan. Wang, Tianlin Li, Yang Liu

Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds (2022.naacl-main)

Challenge:	Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware.
Approach:	They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration.
Outcome:	The proposed framework outperforms expert-designed training strategies within 20 iterations.

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Yu Zhang, Yu Meng, Xuan Wang, Sheng Wang, Jiawei Han

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (2024.emnlp-main)

Challenge:	Existing topic models adopt a fully unsupervised setting and their discovered topics may not reflect user preferences well due to their unsupervised nature.
Approach:	They propose a framework that allows out-of-vocabulary seeds to be used to find latent topics from text corpora.
Outcome:	The proposed framework can find topics that are never seen in the corpus and can benefit from the general knowledge of pre-trained language models.

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Yu Zhang, Xiusi Chen, Bowen Jin, Sheng Wang, Shuiwang Ji, Wei Wang, Jiawei Han

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)

Challenge:	Existing surveys on scientific LLMs focus on one or two fields or a single modality.
Approach:	They survey 260 scientific LLMs and examine their architectures and pre-training techniques . they also discuss commonalities and differences between LLM architectures .
Outcome:	The proposed model architectures and evaluation techniques are used to improve scientific discovery.

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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (2021.acl-long)

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Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong

DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)

Challenge:	Existing methods to detect false claims ignore the characteristics of FC-articles . claims are often quoted to describe checked events, providing lexical information . sentence templates to introduce or debunk claims are common across articles, providing pattern information.
Approach:	They propose a model to rerank FC-articles using key sentences and pattern information.
Outcome:	The proposed model outperforms existing methods on two real-world datasets showing that key sentences can be used to predict if an article fact-checks the given claim.

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Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose

Text Generation

Challenge:	Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making.
Approach:	They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level.
Outcome:	The proposed framework exhibits substantially higher agreement with medical experts than existing metrics.

Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation (2025.findings-emnlp)

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Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang, Mohammed I. Thanoon, Haojie Zhuang, Behnaz Soltani, Munazza Zaib

SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning (2026.acl-long)

Challenge:	Distractors are used to generate plausible but incorrect options for fill-in-the-blank questions . research studies focus on fine-tuning pre-trained models with data augmentation techniques to generate distractors .
Approach:	They propose a model that trains the model to recognize essential semantic features necessary to generate distractors.
Outcome:	The proposed model outperforms existing models on two public datasets.

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Jiaqi Li, Zhijing Zhang, Jiahui Geng, Sheng Bi, Chuanyi Zhang, Fan Liu, Guilin Qi

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)

Challenge:	Existing approaches focus on general utility metrics, overlooking the preservation of semantically related concepts.
Approach:	They propose a method that introduces self-generated proximal visual tokens to prevent forgetting vulnerability.
Outcome:	The proposed framework outperforms existing methods in preserving semantically related concepts while achieving effective target unlearning.

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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong

Scene Restoring for Narrative Machine Reading Comprehension (2020.emnlp-main)

Challenge:	Existing studies on LLM confidence estimations in languages other than English have been limited to English.
Approach:	They propose to use question-related language to prompt LLMs to assess their confidence in large language models.
Outcome:	The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations.

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Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, Zhicheng Sheng

From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning (2025.naacl-long)

Challenge:	Narrative passages describe a chain of events, which helps the machine understand the passage comprehensively.
Approach:	They propose a method to let machine read narrative passages with their prior knowledge . they build a scene graph using Atomic as external knowledge and encode it with GDIN .
Outcome:	The proposed method achieves state-of-the-art on a Story Cloze Test and CosmosQA datasets.

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Nan Xu, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

Low-Resource NLP

Challenge:	Motivated by in-context learning capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities when multiple image-text pairs are provided as demonstrations.
Approach:	They conduct systematic and principled evaluation of multimodal ICL for models of different scales on a broad spectrum of new yet critical tasks.
Outcome:	The proposed model performance improves on a broad spectrum of new yet critical tasks.

Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing (2025.emnlp-main)

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Wenyuan Zhang, Shuaiyi Nie, Jiawei Sheng, Zefeng Zhang, Xinghua Zhang, Yongquan He, Tingwen Liu

MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)

Challenge:	Existing studies on large language models (LLMs) fail to detect character knowledge errors, leading to low-quality automatic corpus construction.
Approach:	They propose to use a large language model to detect known knowledge errors and an agent-based reasoning method to improve error detection.
Outcome:	The proposed method improves the ability of LLMs to detect errors in known knowledge errors and unknown knowledge errors while playing roles.

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Qin Liu, Wenxuan Zhou, Nan Xu, James Y. Huang, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

Joint Universal Syntactic and Semantic Parsing (2021.tacl-1)

Challenge:	Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios.
Approach:	They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task .
Outcome:	Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses .

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Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, Benjamin Van Durme

Syntactic Text Processing

Challenge:	Several attempts have been made to jointly parse syntax and semantics, but this trade-off is not well understood.
Approach:	They propose multiple model architectures that exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics dataset to obtain state-of-the-art results.
Outcome:	The proposed model outperforms existing models in 8 languages and their results are consistent across languages.

SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)

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Xingbo Yao, Xiaoyu Chen, Doudou Zhang, Mingzhi Sheng, Boyuan Cao, Ying-Cong Chen, Hui Xiong

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images.
Approach:	They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image.
Outcome:	The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing (2022.acl-long)

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

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Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Sebastian Schwemer, Anders Søgaard

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

Challenge:	Using pre-trained language models, we evaluate performance group disparities while none of these techniques guarantee fairness, nor consistently mitigate group disparity.
Approach:	They present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.
Outcome:	The proposed methods show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparity.

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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong

Low-Resource NLP

Challenge:	Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity.
Approach:	They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels.
Outcome:	The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency.

Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)

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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo

Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report (D19-60)

Challenge:	Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity.
Approach:	They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability.
Outcome:	The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability.

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Simon Ostermann, Sheng Zhang, Michael Roth, Peter Clark

Dive into Deep Learning for Natural Language Processing (D19-2)

Challenge:	The workshop on Commonsense Inference in NLP (COIN) evaluated text understanding systems' ability to draw inferences about facts that are not mentioned in the text, but that are assumed to be common ground.
Approach:	They propose to use commonsense knowledge to evaluate systems' ability to answer questions/queries about a text.
Outcome:	The proposed tasks evaluated systems in two contexts: Commonsense Inference and Commonsensible Inference.

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Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander Smola

Challenge:	GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP.
Approach:	This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP.
Outcome:	This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP.

Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2025.naacl-long)

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Dongming Sheng, Kexin Han, Hao Li, Yan Zhang, Yucheng Huang, Jun Lang, Wenqiang Liu

Multilinguality

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area . current code-switching methods suffer from term boundary detection issues and out-of-dictionary problems.
Approach:	They propose a test-time code-switching framework which bridges the gap between bilingual training and monolingual test- time prediction.
Outcome:	The proposed framework achieves an average improvement of 3.7% on four cross-lingual datasets.

Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)

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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi

Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)

Challenge:	Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs).
Approach:	They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD).
Outcome:	The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation.

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Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon

RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)

Challenge:	Relation extraction (RE) has been challenging in low-resource domains and with limited resources.
Approach:	They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Outcome:	The proposed method outperforms PLM-based RE classifier on two document-level RE datasets.

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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou

Representation Learning

Challenge:	Traditional metrics for automatic text evaluation are tailored to specific tasks, while LLM-based evaluation metrics are costly.
Approach:	They propose a metric that leverages projections of LLM representations for evaluation.
Outcome:	The proposed metric exhibits higher correlation with human judgments than previous methods on 14 datasets.

EMNLP: Educator-role Moral and Normative Large Language Models Profiling (2025.emnlp-main)

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Yilin Jiang, Mingzi Zhang, Sheng Jin, Zengyi Yu, Xiangjie Kong, Binghao Tu

Broad-Coverage Semantic Parsing as Transduction (D19-1)

Challenge:	Existing frameworks for evaluating the ethical and moral alignment of large language models (LLMs) in educational AI are lacking.
Approach:	They propose a framework for a teacher-role moral and normative LLMs profiling . they extend existing scales and construct 88 teacher-specific moral dilemmas .
Outcome:	The proposed framework evaluates compliance and vulnerability of teacher-role LLMs under soft prompt injection.

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Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)

Challenge:	Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph.
Approach:	They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation.
Outcome:	The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP.

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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

Challenge:	Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances.
Approach:	They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains.
Outcome:	The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement.

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search (2026.acl-long)

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

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Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao

Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking (2025.acl-long)

Challenge:	Existing methods for memory management struggle to capture fine-grained semantic relations between queries and documents.
Approach:	They propose a framework for reasoning and agentic search that grows fine-grained memory fragments from seed tokens from queries, then retraces and deep refines the memory via a contribution function.
Outcome:	Experiments on eight benchmark datasets show that MemSearch-o1 significantly mitigates memory dilution and more effectively activates reasoning potential of diverse LLMs.

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Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)

Challenge:	Large language models face inherent performance bottlenecks under parameter constraints . challenging tokens induce abrupt gradient spikes across layers, exposing stress points .
Approach:	They propose an inner thinking transformer that reimagines layer computations as implicit thinking steps.
Outcome:	Empirical results show that ITT outperforms Transformer/Loop variants in 11 benchmarks.

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Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases.
Approach:	They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP).
Outcome:	The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

Structured Data in NLP

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)

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Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, Yongchun Zhu

Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning (2025.acl-long)

Challenge:	Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated.
Approach:	They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction.
Outcome:	The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results.

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Jiaqi Li, Yanming Li, Xiaoli Shen, Chuanyi Zhang, Guilin Qi, Sheng Bi

Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)

Challenge:	Current AM methods focus on extracting attributes from unimodal text, underutilizing multimodal data.
Approach:	They propose a framework for multimodal self-correction instruction tuning to extract new attributes from images and text with Multimodal Large Language Models.
Outcome:	The proposed framework outperforms state-of-the-art methods on two datasets.

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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)

Challenge:	Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck .
Approach:	They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder .
Outcome:	The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets.

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Bo Li, Wei Ye, Zhonghao Sheng, Rui Xie, Xiangyu Xi, Shikun Zhang

UniSpeaker: A Unified Approach for Multimodality-driven Speaker Generation (2025.findings-emnlp)

Challenge:	Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts.
Approach:	They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training.
Outcome:	The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed.

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Zhengyan Sheng, Zhihao Du, Heng Lu, ShiLiang Zhang, Zhen-Hua Ling

Cross-lingual Decompositional Semantic Parsing (D18-1)

Challenge:	a new framework for speaker generation is proposed to enable multimodal speaker generation . multimodal cues such as visual appearance, textual descriptions, and other biometric signals are still in its early stages.
Approach:	a new framework is proposed to enable multimodal speaker generation . the framework uses self-distillation to apply speaker disentanglement to speech generation a model is developed .
Outcome:	The proposed framework is the first to support unified voice generation from arbitrary modality combinations.

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Sheng Zhang, Xutai Ma, Rachel Rudinger, Kevin Duh, Benjamin Van Durme

mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)

Challenge:	Renewed interest in semantic analysis has led to a surge of proposed new frameworks . many of these efforts are limited to the analysis of English, but with a number of exceptions e.g., recent efforts in Minimal Recursion Semantics (MRS) and multilingual FrameNet annotation and parsing.
Approach:	They propose a cross-lingual decompositional semantic analysis task based on a target language . they propose 'end-to-end' model with an annotating mechanism that supports intra-sentential coreference .
Outcome:	The proposed model outperforms baselines by at least 1.75 F1 score on an evaluation dataset.

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Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen

BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation (2025.emnlp-main)

Challenge:	Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement.
Approach:	They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference.
Outcome:	The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks.

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Tianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang, Dongyeop Kang

Representation Learning

Challenge:	Autoregressive generative models are gaining traction in language tasks such as text generation and machine translation.
Approach:	They propose a likelihood-based evaluation metric that fits transformer-based model embeddings into a stochastic process and propose it as a probability-based metric.
Outcome:	The proposed model embeddings induce a "clustered-to-temporal ordered" mapping of language model representations in high-dimensional space, and this structure enhances performance on tasks such as temporal consistency evaluation and AI-generated content detection.

Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer (2023.findings-emnlp)

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Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao

Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)

Challenge:	Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks.
Approach:	They propose a transformer variant with mixed attention spans that leverages the attention mechanism to capture long- and short-range dependencies in the sequence.
Outcome:	The proposed model can achieve competitive performance to models with full attention while reducing computational cost (75%)

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Hanzhang Yuan, Mengxuan Hu, Wenhao Zhang, Tianlong Wang, Zhongliang Zhou, Jiasen Lu, Sheng Li

Visual Data in NLP

Challenge:	Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts.
Approach:	They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy.
Outcome:	The proposed method selectively removes less informative tokens while maintaining performance.

CoTD-PO: Chain-of-Thought Distillation with Preference Optimization (2025.findings-emnlp)

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Lujie Niu, Haochen Sun, Fangkun Zhao, Sheng Chen, Zimeng Bai, Jiawei Zhang, Caixia Yuan, Xiaojie Wang

Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation (2022.findings-naacl)

Challenge:	Existing methods for chain-of-thought distillation suffer from a distribution mismatch between teacher-generated training trajectories and the student model's own generative distribution.
Approach:	They propose a framework that shifts the training paradigm from passive imitation to active trajectory exploration by allowing students to sample their own answer paths.
Outcome:	The proposed method outperforms standard CoT distillation baselines while mitigating mode collapse and preserving semantic diversity.

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Junwei Yang, Zequn Liu, Ming Zhang, Sheng Wang

Structured Data in NLP

Challenge:	Neural text generation is a novel technique to describe biomedical pathways without manually curation.
Approach:	They propose a new dataset Pathway2Text which contains 2,367 pairs of biomedical pathways and textual descriptions.
Outcome:	The proposed method improves on both Graph2Text and Text2Graph tasks and can be used as a benchmark for biomedical named entity recognition.

DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)

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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li

Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (2022.acl-short)

Challenge:	Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage.
Approach:	They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation.
Outcome:	The proposed model excels on three datasets.

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Miryam de Lhoneux, Sheng Zhang, Anders Søgaard

Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models (2024.findings-emnlp)

Challenge:	Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be effective for cross-lingual transfer of syntactic parsing models but only between related languages.
Approach:	They propose to use multi-task learning to dynamically optimize for parsing performance on outlier languages by using a multi-level learning approach.
Outcome:	The proposed method significantly outperforms uniform and size-proportional sampling in the zero-shot setting.

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Sheng Zhang, Hui Li, Rongrong Ji

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.findings-emnlp)

Challenge:	Code pre-trained language models (CPLMs) are trained on open-source code, raising concerns about data infringement.
Approach:	They propose a framework for detecting unauthorized code use in CPLMs . they use signal extraction from pre-training tasks and weighted inference to identify code membership status accurately.
Outcome:	The proposed framework detects unauthorized code use with high accuracy.

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Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Xue Mengge, Hongbo Xu

AMR Parsing as Sequence-to-Graph Transduction (P19-1)

Challenge:	Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories.
Approach:	They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning.
Outcome:	The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising.

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Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

BTS: Harmonizing Specialized Experts into a Generalist LLM (2025.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics.
Approach:	They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data.
Outcome:	The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%).

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Qizhen Zhang, Prajjwal Bhargava, Chloe Bi, Chris X. Cai, Jakob Nicolaus Foerster, Jeremy Fu, Punit Singh Koura, Ruan Silva, Sheng Shen, Emily Dinan, Suchin Gururangan, Mike Lewis

Locally Aggregated Feature Attribution on Natural Language Model Understanding (2022.naacl-main)

Challenge:	Branch-Train-Stitch (BTS) is an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model.
Approach:	They propose an efficient and flexible training algorithm for combining large language model (LLM) experts into a single, capable generalist model using lightweight stitch layers.
Outcome:	The proposed model can generalize to new domains despite being frozen . it yields the best generalist performance on a variety of downstream tasks, retaining the specialized capabilities of each of the experts.

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Sheng Zhang, Jin Wang, Haitao Jiang, Rui Song

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

Challenge:	a growing popularity of deep-learning models makes model understanding more important . feature attribution methods have shown promising results in computer vision but are not trivial .
Approach:	They propose a gradient-based feature attribution method that smooths gradients by aggregating similar reference texts derived from language model embeddings.
Outcome:	The proposed method outperforms existing methods on public datasets and key words detection tasks.

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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

Information Retrieval

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)

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Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu, Ming Zhang, Sheng Wang

Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval? (2026.findings-acl)

Challenge:	Existing meta-path generation methods cannot fully exploit rich textual information in HINs.
Approach:	They propose a text-infilling-based approach to generate meta-paths from textual information in HINs.
Outcome:	The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths.

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Hengyi Feng, Zeang Sheng, Meiyi Qiang, Yang Li, Wentao Zhang

Supervised Treebank Conversion: Data and Approaches (P18-1)

Challenge:	Rapid advances in multimodal large language models have revolutionized cross-modality understanding.
Approach:	They propose a method that uses whitening transformations to adjust MLLM representation spaces . they propose ML models that are dominated by textual semantics and visual semantics .
Outcome:	The proposed approach improves zero-shot multimodal retrieval performance without fine-tuning efforts.

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Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Syntactic Text Processing

Challenge:	Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time.
Approach:	They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser .
Outcome:	The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%)

Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)

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Kun Liu, Yao Fu, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao

COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context (2026.acl-long)

Challenge:	Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises.
Approach:	They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions.
Outcome:	The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance.

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Guangya Wan, Mingyang Ling, Xiaoqi Ren, Rujun Han, Sheng Li, Zizhao Zhang

Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents.
Approach:	They propose a framework that separates tactical execution, strategic oversight, and context organization into three specialized components.
Outcome:	The proposed framework improves accuracy by 20% relative to baselines on GAIA, BrowseComp, and Humanity’s Last Exam tasks.

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Sheng Liang, Hang Lv, Zhihao Wen, Yaxiong Wu, Yongyue Zhang, Hao Wang, Yong Liu

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.emnlp-main)

Challenge:	Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text.
Approach:	They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation.
Outcome:	The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures.

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Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Xue Mengge, Hongbo Xu

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)

Challenge:	Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories.
Approach:	They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning.
Outcome:	The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising.

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Boheng Sheng, Jiacheng Yao, Meicong Zhang, Guoxiu He

Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)

Challenge:	Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy.
Approach:	They propose a method for dynamically separating and selecting chunks of long context, facilitating a more streamlined input for LLMs.
Outcome:	The proposed approach outperforms baseline methods on single-hop and multi-hop question-answering benchmarks.

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Wei Ye, Bo Li, Rui Xie, Zhonghao Sheng, Long Chen, Shikun Zhang

Neural-Davidsonian Semantic Proto-role Labeling (D18-1)

Challenge:	Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score.
Approach:	They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss.
Outcome:	The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score.

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Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, Benjamin Van Durme

Semantic Parsing

Challenge:	Existing models for semantic proto-role labeling are based on a bidirectional LSTM encoding strategy.
Approach:	They propose a neural model for semantic proto-role labeling using a bidirectional LSTM encoding strategy that is adapted for the task.
Outcome:	The proposed model achieves state-of-the-art in a sentence with a LSTM encoder and a decoder.

Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization (2024.findings-eacl)

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Haojie Zhuang, Wei Emma Zhang, Chang Dong, Jian Yang, Quan Sheng

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing methods for contrastive learning rely on manual negative examples and are poor in quality and adaptability during training.
Approach:	They propose a framework that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization by combining a negative example network and a representation network.
Outcome:	The proposed approach eliminates the need for manual negative example design and improves on two benchmark datasets.

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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang

DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks (2026.acl-long)

Challenge:	Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited.
Approach:	They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments.
Outcome:	Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning.

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Yuqing Zhang, Honghui Sheng, Xueyu Hu, Shengyu Zhang, Fei Wu

Challenge:	Existing benchmarks focus on short, linear workflows and step-level accuracy, highlighting performance degradations.
Approach:	They propose a decision-aware benchmark with compositional tasks comprising 830 episodes and 11,345 action steps across 35 applications on Android and iOS.
Outcome:	The proposed benchmarks show performance degradation and branch correctness issues in 7 different GUI agents.

Sociolectal Analysis of Pretrained Language Models (2021.emnlp-main)

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Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard

Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking (2024.findings-acl)

Challenge:	Using data from English cloze tests, we demonstrate wide performance gaps across demographic groups and show that pretrained language models disfavor young non-white male speakers.
Approach:	They use data from English cloze tests to examine performance differences of pretrained language models across demographic groups.
Outcome:	The models disfavor young non-white male speakers, but larger models reduce performance gaps between majority and minority groups.

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Zefeng Zhang, Jiawei Sheng, Zhang Chuang, Liangyunzhi Liangyunzhi, Wenyuan Zhang, Siqi Wang, Tingwen Liu

Universal Decompositional Semantic Parsing (2020.acl-main)

Challenge:	Existing methods to link ambiguous mentions to entities in multimodal knowledge graphs rely on partial correlations.
Approach:	They propose a framework that leverages multi-element correlations to bridge modality gap and enable fine-grained semantic matching by exploiting correlation between multimodal features and entities.
Outcome:	The proposed framework outperforms state-of-the-art models and confirms the effectiveness of the proposed method.

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Elias Stengel-Eskin, Aaron Steven White, Sheng Zhang, Benjamin Van Durme

Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation (2024.emnlp-main)

Challenge:	Decompositional Semantics is a formalism that encodes semantic information in a feature-based scheme using continuous scales rather than categorical labels.
Approach:	They propose a transductive model for parsing into Universal Decompositional Semantics representations and a pipeline model for annotating the graph with decompositionally semantic attribute scores.
Outcome:	The proposed model performs well while performing attribute prediction.

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Elaf Alhazmi, Quan Sheng, Wei Emma Zhang, Munazza Zaib, Ahoud Alhazmi

Challenge:	Objective questions such as fill-in-the-blank and multiple-choice require examinees to select one valid answer from a set of invalid options.
Approach:	They examine distractor generation tasks, datasets, methods, and evaluation metrics for English objective questions.
Outcome:	The proposed task is based on fill-in-the-blank and multiple choice questions and is widely utilized in educational settings across various domains and subjects.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)

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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao

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

Challenge:	Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL).
Approach:	They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.
Outcome:	The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.

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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.

HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)

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Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng

Natural Language Interfaces

Challenge:	Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval.
Approach:	They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges.
Outcome:	Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations.

Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification (2025.emnlp-main)

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Weiyi Yang, Richong Zhang, Junfan Chen, Jiawei Sheng

Compositional Zero-Shot Domain Transfer with Text-to-Text Models (2023.tacl-1)

Challenge:	Existing studies develop effective pseudo-labeling methods, but they struggle with unlabeled data that have imbalanced classes mismatched with the labeled data.
Approach:	They propose to use pseudo-labeling to train text classification models with few labeled data and massive unlabeled data.
Outcome:	Empirical results show that the proposed model outperforms state-of-the-art methods on 3 common benchmarks.

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Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland

Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)

Challenge:	Existing approaches to zero-shot domain transfer are limited by domain gap and lack of in-domain labels.
Approach:	They propose a compositional transfer learning framework (DoT51) that learns domain knowledge and task knowledge in a multi-task manner without access to in-domain labels.
Outcome:	The proposed framework outperforms the current state-of-the-art in zero-shot domain transfer by over 7 absolute points in accuracy on RadNLI.

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Haojie Zhuang, Wei Emma Zhang, Jian Yang, Congbo Ma, Yutong Qu, Quan Z. Sheng

SecCoder: Towards Generalizable and Robust Secure Code Generation (2024.emnlp-main)

Challenge:	Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult.
Approach:	They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary.
Outcome:	The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents.

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Boyu Zhang, Tianyu Du, Junkai Tong, Xuhong Zhang, Kingsum Chow, Sheng Cheng, Xun Wang, Jianwei Yin

Programming Languages in NLP

Challenge:	Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation.
Approach:	They propose a generalizable and robust secure code generation method SecCoder by using in-context learning and the safe demonstration.
Outcome:	The proposed method achieves a significant security improvement of 7.20% on unseen test cases and better robustness against the attacked model.

Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)

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Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon

Context-faithful Prompting for Large Language Models (2023.findings-emnlp)

Challenge:	Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans .
Approach:	They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics.
Outcome:	The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points.

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Wenxuan Zhou, Sheng Zhang, Hoifung Poon, Muhao Chen

Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information (2025.emnlp-main)

Challenge:	Large language models encode parametric knowledge about world facts but overly rely on it can cause incorrect predictions in context-sensitive NLP tasks.
Approach:	They propose to use opinion-based prompts and counterfactual demonstrations to improve LLM faithfulness to contexts.
Outcome:	The proposed methods improve faithfulness to contexts using opinion-based prompts and counterfactual demonstrations.

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Yao Chen, Jiawei Sheng, Wenyuan Zhang, Tingwen Liu

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge (2023.findings-acl)

Challenge:	Existing methods focus on transferring teacher-generated rationales to student models, but do not explore teachers’ dynamic attention towards critical information during reasoning.
Approach:	They propose a method that transfers the teacher’s stepwise attention on key information to the student model and a Mixture of Layers module that allows dynamic alignment between the teacher and student.
Outcome:	The proposed framework achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets.

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Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henghui Zhu, Yuhao Zhang, Alexander Hanbo Li, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)

Challenge:	Open-ended Visual Question Answering (VQA) requires models to reason over visual and natural language inputs using world knowledge.
Approach:	They propose a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time.
Outcome:	The proposed pipeline expands the knowledge coverage from in-domain training data by 4.1% on OK-VQA, without additional computation cost.

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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme

Structured Data in NLP

Challenge:	Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement.
Approach:	They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer .
Outcome:	The dataset is bundled with the Decomp toolkit (v0.1) both datasets are publicly available at http://decomp.io.

Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)

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Ye Liu, Sheng Zhang, Rui Song, Suo Feng, Yanghua Xiao

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)

Challenge:	Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing.
Approach:	They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities.
Outcome:	The proposed framework outperforms baselines by 16.5 - 27.8%.

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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects (2026.findings-eacl)

Challenge:	Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns.
Approach:	They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness.
Outcome:	The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM .

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Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang, Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng