Papers by Yi Chang

Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem (2026.findings-acl)

Challenge:	Existing methods for analyzing and utilizing toxic samples are limited . current methods fail to fully harness their potential .
Approach:	They propose a diverse detoxification framework that leverages toxic samples' diversity . they propose MPSG strategy and SC-DPO approach to elicit personalized toxic responses .
Outcome:	The proposed framework could be used to optimize large language models for user safety . it incorporates two components: MPSG strategy and SC-DPO approach .

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Lichang Song, Ting Long, Yi Chang

Information Retrieval

Challenge:	Existing RAG systems rely on ranking-centric, asymmetric dependency paradigms to generate results.
Approach:	They propose a framework that treats the reranker and the generator as peer decision-makers rather than being connected through an asymmetric dependency pipeline.
Outcome:	The proposed framework treats the reranker and the generator as peer decision-makers rather than being connected through an asymmetric dependency pipeline.

AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training (2026.findings-acl)

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Zhiyuan Li, Yuan Wu, Yi Chang

Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)

Challenge:	Adaptive group-wise gradient clipping (AGGC) is a new approach to stabilize training of Large Language Models.
Approach:	They propose a method to stabilize gradient clipping by partitioning parameters into groups based on functional types and a time-dependent scheduling mechanism to balance exploration and convergence.
Outcome:	The proposed algorithm outperforms standard LoRA and achieves 72.93% accuracy . it can be integrated into existing pipelines with negligible overhead.

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Zhiyuan Chang, Mingyang Li, Junjie Wang, Yi Liu, Qing Wang, Yang Liu

One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments (2025.acl-long)

Challenge:	Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach:	They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome:	The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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Ke Yi, Yuhui Xu, Heng Chang, Yuan Meng, Tong Zhang, Jia Li

Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)

Challenge:	Quantization has shown promise for Large Language Models, but current methods require lengthy training to alleviate quantization loss.
Approach:	They propose to decouple weights and incorporate Low-Rank adapters to reduce weight sharing . they validate the approach on LLaMA2 families and Mistral on downstream evaluation .
Outcome:	The proposed approach shows high performance while reducing deployment time faced with multiple scenarios.

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Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, Yi Chang

Sentiment Analysis

Challenge:	Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis.
Approach:	They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity.
Outcome:	The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated.

When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)

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Liang-Bo Ning, Yuchen Zhu, Heqing Huang, Xin Wang, Yi Chang, Li Qing, Wenqi Fan

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)

Challenge:	Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost.
Approach:	They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations.
Outcome:	The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations.

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Tingyu Xia, Yue Wang, Yuan Tian, Yi Chang

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks.
Approach:	They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus .
Outcome:	The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks.

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

Document Modeling with External Attention for Sentence Extraction (P18-1)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang

Information Extraction & Text Mining

Challenge:	Document modeling is essential to a variety of natural language understanding tasks.
Approach:	They propose to use external information to improve document modeling for sentence extraction problems.
Outcome:	The proposed model outperforms baseline models on document summarization and answer selection tasks and achieves state-of-the-art results on WikiQA and NewsQA.

VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)

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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou

Visual Data in NLP

Challenge:	Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy.
Approach:	They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems.
Outcome:	The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better.

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)

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Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang

Syntactic Text Processing

Challenge:	Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost.
Approach:	They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages .
Outcome:	The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%.

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)

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Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)

Challenge:	Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs.
Approach:	They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.
Outcome:	The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.

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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

Challenge:	Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases.
Approach:	They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens.
Outcome:	The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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Jinnan Li, Jinzhe Li, Yue Wang, Yi Chang, Yuan Wu

Dialogue Systems & Conversational Agents

Challenge:	Existing evaluation benchmarks focus on fine-grained constraint satisfaction and domain-specific capability assessment, yet overlook the crucial structural dependencies between dialogue turns that distinguish multi-turn from single-turn interactions.
Approach:	They propose a multi-turn instruction following benchmark with structural flow modeling that defines an innovative structural flow framework with six fundamental inter-turn relationships.
Outcome:	The proposed model is based on a framework with six fundamental inter-turn relationships and is able to analyze and generate specific dialogue flows tailored to specific scenarios.

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

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

MACAROON: Training Vision-Language Models To Be Your Engaged Partners (2024.findings-emnlp)

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

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Shujin Wu, Yi Fung, Sha Li, Yixin Wan, Kai-Wei Chang, Heng Ji

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

Challenge:	Large vision-language models (LVLMs) generate detailed responses even when questions are ambiguous or unanswerable, leading to hallucinations and bias issues.
Approach:	They propose a three-tiered hierarchy for questions of invalid, ambiguous, and personalizable nature to measure the proactive engagement capabilities of LVLMs.
Outcome:	The proposed model generates contrastive response pairs for unlabeled questions, achieving 0.84 AAR, while maintaining comparable performance on general tasks.

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

Large Language Model Evaluation via Matrix Nuclear-Norm (2025.findings-emnlp)

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

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Yahan Li, Tingyu Xia, Yuan Wu, Yi Chang

On the Transferability of Adversarial Attacks against Neural Text Classifier (2021.emnlp-main)

Challenge:	Large language models (LLMs) are computationally intensive due to their O(n3) time complexity with Singular Value Decomposition (SVD).
Approach:	They propose a metric to quantify the data compression proficiency of large language models and a convex approximation of matrix rank to capture both predictive discriminability and diversity.
Outcome:	The proposed model achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as models increase from 111M to 6.7B .

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Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang

Robustness in NLP

Challenge:	Existing studies show that deep neural networks are vulnerable to adversarial examples . a small perturbation to an input alters the model prediction .
Approach:	They propose a genetic algorithm to find models that can induce adversarial examples to fool models . they propose word replacement rules that can be used for model diagnostics from these examples .
Outcome:	The proposed model can fool almost all existing models, while ignoring the data bias in the training set.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

Structured Data in NLP

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning (2025.findings-acl)

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Chenyang Bu, Guojie Chang, Zihao Chen, CunYuan Dang, Zhize Wu, Yi He, Xindong Wu

Training-free LLM Merging for Multi-task Learning (2025.acl-long)

Challenge:	Existing approaches to build knowledge graphs with LLMs are constrained by static knowledge bases and ineffective multimodal data integration.
Approach:	They propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics.
Outcome:	The proposed framework outperforms unsupervised competitors in cross-modal understanding of complex queries.

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Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao

Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing tasks.
Approach:	They propose a training-free method for unifying different specialized LLMs into a single model using model-wise and layer-wise pruning and scaling.
Outcome:	The proposed method outperforms existing merging techniques and surpasses models fine-tuned on combined datasets in most scenarios.

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Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, Ming-Wei Chang

BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards (2026.findings-acl)

Challenge:	Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts.
Approach:	They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge .
Outcome:	The proposed dataset elicits models to use fine-grained knowledge learned during pre-training.

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Yupeng Chang, Yuan Wu, Yi Chang

Abstract Meaning Representation for Paraphrase Detection (N18-1)

Challenge:	Critic-free reinforcement learning with verifiable rewards (RLVR) is a practical paradigm for aligning Large Language Models.
Approach:	They propose a framework that stabilizes advantage estimation by combining prompt-local on-policy statistics with semantic-cluster-conditioned historical moments.
Outcome:	Experiments show that RLVR improves training stability and performance compared to critic-based methods . compared with other approaches, RL VR improves in cold-start regimes with binary verifiers .

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Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)

Challenge:	Abstract Meaning Representation (AMR) parsing is ideal for paraphrase detection . it abstracts away from the syntactic realization of a sentence, and denotes only its meaning in a canonical form.
Approach:	They propose a technique that uses latent semantic analysis to translate sentences into AMR graphs . they show that the technique can be used to detect whether two sentences have the same meaning .
Outcome:	The proposed technique significantly advances state-of-the-art paraphrase detection for the Microsoft Research Paraphrase Corpus.

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Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen

Robustness in NLP

Challenge:	Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content.
Approach:	They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts.
Outcome:	Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods.

Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)

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Jiankun Zhang, Shenglai Zeng, Jie Ren, Tianqi Zheng, Hui Liu, Xianfeng Tang, Hui Liu, Yi Chang

InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (2021.acl-long)

Challenge:	Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities.
Approach:	They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content .
Outcome:	The proposed approach generates outputs resembling retrieved content and exposes sensitive information.

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Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil

LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization (2025.findings-emnlp)

Challenge:	a novel approach to detect fake news is needed due to training data scarcity . current methods focus on document-level fake news detection using lexical features and semantic embeddings .
Approach:	They propose a novel benchmark for fake news detection at the knowledge element level . they propose synthesis method which manipulates knowledge elements to generate noisy training data .
Outcome:	The proposed method outperforms the state-of-the-art in detecting misinformation . it yields fine-grained explanations and outperformed the current methods .

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Yupeng Chang, Chenlu Guo, Yi Chang, Yuan Wu

Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble (2021.acl-long)

Challenge:	Low-Rank Adaptation (LoRA) adapts large language models by training only a small fraction of parameters, but as the rank of the low-rank matrices increases, LoRA exhibits an unstable “double descent” phenomenon, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima.
Approach:	They propose a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO) MGPO stabilizes training dynamics by mitigating double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state.
Outcome:	The proposed framework improves performance on natural language understanding benchmarks and shows that it improves convergence and generalization.

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Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang

Robustness in NLP

Challenge:	Recent studies show vulnerability of deep neural networks to adversarial examples that intentionally fool the networks.
Approach:	They propose a method for training a robust model to defense synonym substitution-based attacks by sampling embedding vectors for each word in an input sentence and augmenting them with the training data.
Outcome:	The proposed method outperforms other proposed defense methods by a significant margin across different network architectures and multiple data sets.

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

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

A Survey of Retentive Network (2026.findings-acl)

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

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Haiqi Yang, Zhiyuan Li, Yi Chang, Yuan Wu

Challenge:	Existing studies on the effectiveness of the Retentive Networks have not yet been conducted.
Approach:	They propose a retention mechanism that integrates the inductive bias of recurrent neural networks with the parallelizable training advantages of attention-based models.
Outcome:	The proposed retention mechanism combines the inductive bias of recurrent neural networks with the parallelizable training advantages of attention-based models.

ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)

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Erxin Yu, Wenjuan Han, Yuan Tian, Yi Chang

Information Extraction & Text Mining

Challenge:	Existing methods to find relational facts from texts lack hierarchical information of relations.
Approach:	They propose a hierarchical classification framework which extracts relation in a top-down manner.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora .

Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters (2024.emnlp-main)

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Euiin Yi, Taehyeon Kim, Hongseok Jeung, Du-Seong Chang, Se-Young Yun

Multilinguality

Challenge:	Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings.
Approach:	They propose a training recipe for an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM.
Outcome:	The proposed model significantly speeds up inference time and out-of-domain speedup across various languages.

Nonparametric Decoding for Generative Retrieval (2023.findings-acl)

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Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo

Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)

Challenge:	Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed.
Approach:	They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds.
Outcome:	The proposed model can utilize parametric and nonparametric space.

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Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang, Chang Zhou

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling (2026.acl-long)

Challenge:	Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment.
Approach:	They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs.
Outcome:	The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger.

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Yupeng Chang, Yuan Wu, Yi Chang

Reasoning

Challenge:	Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method for large language models.
Approach:	They propose a drop-in extension that reparameterizes a rank-rtot update as a sum of K static low-rank experts.
Outcome:	Experiments on reasoning and knowledge-intensive benchmarks show consistent gains over matched-budget LoRA.

Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models (2025.findings-emnlp)

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Jinzhe Li, Gengxu Li, Yi Chang, Yuan Wu

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)

Challenge:	Existing studies assess LLMs’ reasoning ability in ideal settings, ignoring their vulnerabilities when faced with flawed premises.
Approach:	They propose to evaluate LLMs' ability to proactively identify and articulate errors in input premises.
Outcome:	The proposed model enables LLMs to proactively identify and articulate errors in input premises.

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Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang

Sentiment Analysis

Challenge:	Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect.
Approach:	They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment.
Outcome:	The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.

MicroEdit: Neuron-level Knowledge Disentanglement and Localization in Lifelong Model Editing (2025.emnlp-main)

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Shiqi Wang, Qi Wang, Runliang Niu, He Kong, Yi Chang

Challenge:	Existing methods for enhancing large language models are designed for single or limited edits, lacking the capacity to support long-term, multi-round knowledge updates.
Approach:	They propose a neuron-level editing method that performs minimal interventions within large language models (LLMs) by leveraging a sparse autoencoder, MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates.
Outcome:	Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaM and Mistral.

Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)

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Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip Yu

Sentiment Analysis

Challenge:	Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved.
Approach:	They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available.
Outcome:	The proposed model can discriminate emerging intents when no labeled utterances are available in training data.

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2020.acl-main)

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Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, Yi Chang

Information Extraction & Text Mining

Challenge:	Existing approaches to extract relational triples from unstructured text are inadequate to solve the overlapping triple problem.
Approach:	They propose a cascade binary tagging framework that models relations as functions that map subjects to objects in a sentence.
Outcome:	The proposed framework outperforms state-of-the-art methods on two datasets . it outperformed baseline methods by 17.5 and 30.2 absolute gains .

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables (2022.emnlp-main)

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Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang

Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning (2024.findings-acl)

Challenge:	Recent discrete latent variable models have received a surge of interest in both NLP and CV . they are comparable to the continuous counterparts in representation learning, but are more interpretable in their predictions.
Approach:	They develop a topic-informed discrete latent variable model for semantic textual similarity . they inject the quantized representation into a transformer-based language model .
Outcome:	The proposed model outperforms strong baselines in semantic textual similarity tasks.

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Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji

R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-task Learning (2025.findings-emnlp)

Challenge:	LVLMs are known for producing text that is factually inconsistent with visual input . factuality of generated captions for structured visuals has not been studied as much .
Approach:	They propose a typology of factual errors in captions generated by large vision-language models . they propose CHOCOLATE, a visual entailment model that outperforms current models based on this analysis .
Outcome:	The proposed model outperforms current models in evaluating caption factuality.

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Jinda Liu, Yi Chang, Yuan Wu

ASQA: Factoid Questions Meet Long-Form Answers (2022.emnlp-main)

Challenge:	Low-Rank Adaptation (LoRA) improves performance in multi-task learning by diversifying the head matrices through Multi-Head Dropout and Multi-head Random Initialization.
Approach:	They propose a low-rank adaptive approach to fine-tune large language models by approximating weight updates through low-ranked matrices.
Outcome:	The proposed approach improves performance in multi-task learning while reducing memory usage and training time.

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Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

Question Answering

Challenge:	Recent progress on factoid question answering (QA) does not easily transfer to the task of long-form QA where the goal is to generate detailed explanations.
Approach:	They propose a task that focuses on ambiguous factoid questions which have different correct answers depending on interpretation.
Outcome:	The proposed metric is reliable and demonstrates agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.

NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models (2025.findings-emnlp)

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Chenlu Guo, Yi Chang, Yuan Wu

Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)

Challenge:	Parameter-efficient fine-tuning is essential for adapting large language models (LLMs). However, LoRA suffers from slow convergence and some recent LoRA variants, such as PiSSA, rely on Singular Value Decomposition (SVD) for initialization.
Approach:	They propose to introduce a small intermediate matrix between the low-rank matrices (A) and (B) and propose NyströmLoRA (NLoRA) which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency.
Outcome:	The proposed approach improves on 5 natural language generation tasks and 8 natural language understanding tasks with minimal parameter overhead.

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Zhepei Wei, Yue Wang, Jinnan Li, Zhining Liu, Erxin Yu, Yuan Tian, Xin Wang, Yi Chang

SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)

Challenge:	Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods.
Approach:	They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models.
Outcome:	Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data .

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FU Yuqing, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao

Information Retrieval

Challenge:	Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results.
Approach:	They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model.
Outcome:	The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model .

Enhanced Chart Understanding via Visual Language Pre-training on Plot Table Pairs (2023.findings-acl)

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Mingyang Zhou, Yi Fung, Long Chen, Christopher Thomas, Heng Ji, Shih-Fu Chang