Papers by Yun Yang

Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have increased the vulnerability of LLMs, but they can cause more severe damage than standalone systems if compromised.
Approach:	They propose a new type of attack that induces malfunctions by misleading the agent into executing repetitive or irrelevant actions.
Outcome:	The proposed attacks induce failure rates exceeding 80% in multiple scenarios, highlighting the substantial risks associated with this vulnerability.

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Zeguan Xiao, Yan Yang, Guanhua Chen, Yun Chen

G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)

Challenge:	Commercial large language models (LLMs) have made great progress in various NLP tasks.
Approach:	They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing.
Outcome:	The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies.

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Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, Guanhua Chen

Challenge:	Existing approaches to enhance output diversity but compromise quality of outputs.
Approach:	They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality.
Outcome:	The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality.

WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations (2026.findings-acl)

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Jaeyeon Kim, Heeseung Yun, Tony Woo, Chao-Han Huck Yang, Gunhee Kim

Speech & Audio in NLP

Challenge:	Large audio-language models extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection, remains underexplored.
Approach:	They propose a global benchmark to evaluate low-level auditory perception and cognition using marine mammal vocalizations to better assess models’ low- level listening.
Outcome:	The proposed models show performance far below human levels, indicating a need for stronger auditory grounding in LALMs.

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen

Structured Data in NLP

Challenge:	Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach:	They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome:	The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.

ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs (2025.acl-long)

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Yan Yang, Yixia Li, Hongru Wang, Xuetao Wei, James Jianqiao Yu, Yun Chen, Guanhua Chen

Representation Potentials of Foundation Models for Multimodal Alignment: A Survey (2025.emnlp-main)

Challenge:	Recent approaches to reduce resource requirements for task-specific large language models have been developed.
Approach:	They propose a delta compression approach that optimizes for importance of a model . they use SVD to dynamically adjust the sparsity ratios of different vectors based on their importance .
Outcome:	The proposed approach achieves state-of-the-art in retaining task-specific knowledge even at high sparsity ratios.

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Jianglin Lu, Hailing Wang, Yi Xu, Yizhou Wang, Kuo Yang, Yun Fu

Representation Learning

Challenge:	foundation models learn highly transferable representations through large-scale pretraining on diverse data.
Approach:	They examine the representation potentials of foundation models by examining their latent capacity to capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modalities.
Outcome:	The foundation models exhibit remarkable similarities across architectures and modalities, the authors show . the models can capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modality.

SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks (2026.acl-long)

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Tianyi Wang, Yixia Li, Long Li, Yibiao Chen, Shaohan Huang, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms (2026.findings-acl)

Challenge:	Proximal Policy Optimization (PPO) is central to aligning Large Language Models with verifiable rewards.
Approach:	They propose a scalable algorithm that harmonizes sample efficiency with stability of outcome-based updates.
Outcome:	The proposed algorithm outperforms standard PPO and matches the performance of computation-heavy group-based methods.

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Zeguan Xiao, Yun Chen, Jian Yang, Guanhua Chen, Ke Tang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, including instruction following, mathematical problem solving, and coding generation.
Approach:	They propose a method that truncates both preferred and dispreferred responses to match the shorter one’s length.
Outcome:	The proposed approach improves over standard implementations and achieves 11.8 points in AlpacaEval 2 and overall improvements across downstream tasks.

Revealing the Seen, Imagining the Beyond: A Survey of Image-Grounded Chain-of-Thought Reasoning in Multimodal LLMs (2026.acl-long)

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Qihua Dong, Yitian Zhang, Huimin Zeng, Yizhou Wang, Jianglin Lu, Kuo Yang, Yun Fu

DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning.
Approach:	They synthesize the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT) they propose a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model is based on a method-centric taxonomy and benchmarks.

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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh

Composite Backdoor Attacks Against Large Language Models (2024.findings-naacl)

Challenge:	Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts.
Approach:	They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy.
Outcome:	Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.

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Hai Huang, Zhengyu Zhao, Michael Backes, Yun Shen, Yang Zhang

ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated superior performance on various tasks, but untrustworthy third-party LLMs may covertly introduce vulnerabilities for downstream tasks.
Approach:	They propose a composite backdoor attack that scatters multiple trigger keys in different prompt components.
Outcome:	The proposed attack achieves 100% Attack Success Rate (ASR) with a False Triggered Rate (FTR) below 2.06% and negligible model accuracy degradation.

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Jiani Guo, Zuchao Li, Jie Wu, Qianren Wang, Yun Li, Lefei Zhang, Hai Zhao, Yujiu Yang

Efficient Training for Cross-lingual Speech Language Models (2026.findings-acl)

Challenge:	Experimental results show ToM outperforms existing divide-and-conquer frameworks . RAG relies on similarity-based rankings to retrieve and reason over chunks based on logical coherence .
Approach:	They propose a Tree-oriented MapReduce framework for long-context reasoning . it leverages the hierarchical structure of long documents by constructing a DocTree .
Outcome:	Experimental results show that ToM outperforms existing divide-and-conquer frameworks and RAGs . the proposed framework improves logical coherence and long-context reasoning on 70B+ LLMs compared to existing approaches .

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Yan Zhou, Qingkai Fang, Yun Hong, Yang Feng

SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)

Challenge:	Currently, large language models (LLMs) focus on the text modality, making speech modeling difficult.
Approach:	They propose a cross-lingual speech language model that trains on discrete speech tokens to achieve cross-modal and cross-linguistic alignment through continual pre-training.
Outcome:	The proposed method achieves cross-modal and cross-lingual alignment through continual pre-training.

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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao

Challenge:	Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions.
Approach:	They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels .
Outcome:	The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing.

Agentic Episodic Control (2026.findings-acl)

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Xidong Yang, Wenhao Li, Junjie Sheng, Yun Hua, Haosheng Chen, Chuyun Shen, Xiangfeng Wang

Green & Sustainable NLP

Challenge:	Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization.
Approach:	They propose a novel architecture that integrates large language models into episodic RL.
Outcome:	The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success.

Accurate Word Alignment Induction from Neural Machine Translation (2020.emnlp-main)

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Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, Qun Liu

KeFVP: Knowledge-enhanced Financial Volatility Prediction (2023.findings-emnlp)

Challenge:	Prior work suggests that Transformer captures poor word alignments through its attention mechanism.
Approach:	They propose two new word alignment induction methods that use attention weights to capture accurate word alignments.
Outcome:	The proposed methods outperform baselines on three publicly available datasets and are significantly better than GIZA++.

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Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, Weizu Yang

Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)

Challenge:	Current studies ignore the role of financial metrics knowledge in earnings calls and little consideration is given to integrating text and price information.
Approach:	They propose to integrate financial metrics knowledge into text comprehension by knowledge-enhanced adaptive pre-training and effectively incorporating text and price information by introducing a conditional time series prediction module.
Outcome:	The proposed method outperforms state-of-the-art methods on three real-world datasets and is effective and reliable.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Jie Zhou, Yue Zhang

FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

Challenge:	Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents.
Approach:	They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data.
Outcome:	The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences.

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Yutao Hou, Yihan Jiang, Yuhan Xie, Jian Yang, Liwen Zhang, Hailiang Huang, Guanhua Chen, Yun Chen

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

Challenge:	Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach:	They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome:	The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Self-Training Elicits Concise Reasoning in Large Language Models (2025.findings-acl)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun

Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation (2026.findings-acl)

Challenge:	Chain-of-thought reasoning has enabled large language models to use additional computation through intermediate tokens to solve complex tasks, but current models often generate more tokens than necessary to accomplish the task, incurring extraneous inference costs.
Approach:	They propose to fine-tune models with self-generated concise reasoning paths obtained by best-of-N sampling and few-shot conditioning in task-specific settings to elicit concise reasoning.
Outcome:	The proposed method reduces output tokens by 30% on GSM8K and MATH while maintaining average accuracy.

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Kyudan Jung, Hojun Cho, Jooyeol Yun, Soyoung Yang, Jaehyeok Jang, Jaegul Choo

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

Challenge:	Talk-to-Your-Slides is a high-efficiency slide editing agent that uses language-driven structured data manipulation instead of the image modality.
Approach:	They propose a language-driven slide editing agent that uses language-based structured data manipulation instead of image modality.
Outcome:	The proposed system achieves faster processing and better instruction fidelity than GUI-based agents.

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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen

Structured Data in NLP

Challenge:	Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach:	They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome:	The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .

CLeVeR: Multi-modal Contrastive Learning for Vulnerability Code Representation (2025.findings-acl)

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Jiayuan Li, Lei Cui, Sen Zhao, Yun Yang, Lun Li, Hongsong Zhu

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

Challenge:	Existing methods for detecting code capture the overall semantics of the code rather than its intrinsic vulnerability-specific semantics.
Approach:	They propose an approach that leverages contrastive learning to generate precise vulnerability code representations under the supervision of vulnerability descriptions.
Outcome:	The proposed approach outperforms state-of-the-art methods in vulnerability detection tasks by 11.85% and 13.61%.

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

Reasoning

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)

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ChengYan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma

Sentiment Analysis

Challenge:	Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction.
Approach:	This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies .
Outcome:	The survey examines the effectiveness of MERC and its evaluation strategies.

TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025.emnlp-main)

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Yanshu Li, Jianjiang Yang, Tian Yun, Pinyuan Feng, Jinfa Huang, Ruixiang Tang

Chinese Idiom Paraphrasing (2023.tacl-1)

Challenge:	Multimodal in-context learning (ICL) is a key mechanism for harnessing the capabilities of large vision–language models.
Approach:	They propose a transformer-based model with task-aware attention that dynamically configures ICL sequences.
Outcome:	Experiments on five LVLMs and nine datasets show that TACO surpasses baselines across diverse ICL tasks.

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

Text Generation

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

Representation-Guided Parameter-Efficient LLM Unlearning (2026.findings-acl)

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Zeguan Xiao, Lang Mo, Yun Chen, Lei Yang, Jiehui Zhao, Lili Yang, Guanhua Chen

SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)

Challenge:	Existing methods to unlearning large language models often memorize sensitive or harmful information, but they struggle with the forget-retain trade-off due to the polysemantic nature of LLMs parameters.
Approach:	They propose a representation-guided low-rank unlearning approach that leverages the geometric properties of representation spaces to achieve robust and precise unlearning.
Outcome:	The proposed approach outperforms state-of-the-art models on TOFU and WMDP benchmarks while maintaining higher model utility.

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Yan Yang, Zeguan Xiao, Xin Lu, Hongru Wang, Xuetao Wei, Hailiang Huang, Guanhua Chen, Yun Chen

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks.
Approach:	They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically.
Outcome:	The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important .
Approach:	They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment .
Outcome:	The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility .

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Information Retrieval

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

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

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

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)

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

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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao

Green & Sustainable NLP

Challenge:	Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
Approach:	They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory.
Outcome:	The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%.

Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)

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Hao Niu, Maoyi Wang, Yun Xiong, Biao Yang, Xing Jia, Zhonglei Guo

Sentiment Analysis

Challenge:	incorporating structure information can improve the performance of aspect-based sentiment analysis.
Approach:	They propose a method to conduct neuron-level manipulations on word representations in the frequency domain.
Outcome:	The proposed method can achieve or come close to state-of-the-art in ABSA.

Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)

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Cunliang Kong, Yun Chen, Hengyuan Zhang, Liner Yang, Erhong Yang

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them.
Approach:	They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts.
Outcome:	The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning (2023.findings-emnlp)

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Yongjin Yang, Joonkee Kim, Yujin Kim, Namgyu Ho, James Thorne, Se-Young Yun

Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models (2024.emnlp-main)

Challenge:	Recent benchmarks have attempted to identify and explain hate speech but lack the reasoning to supervise detection models.
Approach:	They propose a framework that uses large language models to fill in the gaps in hate speech explanations by using existing annotations.
Outcome:	The proposed framework outperforms baselines on SBIC and Implicit Hate using model-generated data and improves generalization to unseen datasets.

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Yongjin Yang, Jongwoo Ko, Se-Young Yun

Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences (2022.naacl-main)

Challenge:	Vision-language models (VLMs) have demonstrated remarkable applicability across downstream tasks, including zero-shot image classification.
Approach:	They propose an efficient transfer learning method that integrates visual prompts and text adapters with pre-trained VLMs to achieve optimal performance for any target domain.
Outcome:	The proposed method outperforms baselines on unseen tasks.

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Yifan Chen, Qi Zeng, Dilek Hakkani-Tur, Di Jin, Heng Ji, Yun Yang

Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem (2026.acl-long)

Challenge:	Existing models for long sequences are not efficient due to the quadratic space and time complexity of the self-attention modules.
Approach:	They propose to reduce the quadratic complexity to linear (modulo logarithmic factors) by low-dimensional projection and row selection.
Outcome:	The proposed methods outperform transformer-based models with smaller time/space footprint on the Long Range Arena benchmark.

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Zeguan Xiao, Siqing Li, Yong Wang, Xuetao Wei, Jian Yang, Yun Chen, Guanhua Chen

FreezeEmpath: Efficient Training for Empathetic Spoken Chatbots with Frozen LLMs (2026.findings-acl)

Challenge:	Large language models (LLMs) are inherently dual-use and can be leveraged for both beneficial and harmful purposes.
Approach:	They propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient extraction from conflict-aware combination.
Outcome:	The proposed method achieves tighter alignment on WMDP Bio and RWKU benchmarks.

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Yun Hong, Yan Zhou, Yang Feng

Natural Language Interfaces

Challenge:	Recent research has made significant progress in developing empathetic spoken chatbots based on large language models (LLMs).
Approach:	They propose an end-to-end empathetic spoken chatbot trained efficiently that generates emotionally expressive speech and outperforms other emmpathetic models in emphatic dialogue, SER, and SpokenQA tasks.
Outcome:	The proposed model outperforms other empathetic models on e-dialog, SER, and SpokenQA tasks and achieves strong results on several speech tasks.

Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL (2026.findings-acl)

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Hanqing Wang, Yongdong Chi, Jian Yang, Lei Yang, Jiehui Zhao, Yun Chen, Guanhua Chen

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have been successful in Text-to-SQL tasks, but their deployment in real-world environments is hindered by latent reliability issues.
Approach:	They propose a framework to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation.
Outcome:	The proposed framework uncovers a substantial number of failure cases on state-of-the-art open-source LLMs.

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

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

Natural Language Interfaces

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

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)

Challenge:	Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach:	They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome:	The proposed framework captures the trade-off between information relevance and structural complexity.

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Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives .
Approach:	They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding.
Outcome:	The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions (2025.findings-acl)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides PhD, Logan Douglas Schneider, Isaac Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja Mataric

Natural Language Interfaces

Challenge:	Structured data is generated grounded in health and lifestyle factors and full profiles of synthetic users are developed conditioned on the structured data.
Approach:	They propose an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching.
Outcome:	The proposed framework is validated in the domains of sleep and diabetes coaching using two independently-developed agents for sleep and diabetic coaching as case studies.

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages (2025.findings-emnlp)

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Yongdong Chi, Hanqing Wang, Yun Chen, Yan Yang, Jian Yang, Zonghan Yang, Xiao Yan, Guanhua Chen

Programming Languages in NLP

Challenge:	Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large semantic gap between the texts and the low-resource SQL programs.
Approach:	They propose to use Python as a pivot to bridge between natural language query and SQL program.
Outcome:	The proposed method improves the execution accuracy of the best-performing baseline by up to 3.20.

When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs (2025.acl-long)

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Xinyue Shen, Yun Shen, Michael Backes, Yang Zhang