Papers by Shuai Liu

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy (2021.emnlp-main)

Challenge:	Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness .
Approach:	They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling.
Outcome:	The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs.

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Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy

Text Generation

Challenge:	Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks.
Approach:	They propose an architecture-independent approach for leveraging syntactic hierarchies of source code . they use syntax trees to extract syntak hierarchical structures and integrate them into context window .
Outcome:	The proposed approach achieves state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark.

AdDriftBench: A Benchmark for Detecting Data Drift and Label Drift in Short Video Advertising (2025.findings-emnlp)

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Yinghao Song, Xiangji Zeng, Shuai Cui, Lu Sun, Zhaowei Liu, Yuan Yuan, Yulu Wang, Hai Zhou, Zhaohan Gong

RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)

Challenge:	Short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD).
Approach:	They propose to use data drift and label drift to evaluate models under rapidly shifting content distributions and labeling scenarios to assess their generalization capabilities.
Outcome:	The proposed model performs moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions.

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Shuai Liu, Hyundong Cho, Marjorie Freedman, Xuezhe Ma, Jonathan May

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences.
Approach:	They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively.
Outcome:	The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations.

Chain-of-Jailbreak Attack for Image Generation Models via Step by Step Editing (2025.findings-acl)

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Wenxuan Wang, Kuiyi Gao, Youliang Yuan, Jen-tse Huang, Qiuzhi Liu, Shuai Wang, Wenxiang Jiao, Zhaopeng Tu

Can’t See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs (2025.acl-long)

Challenge:	Text-based image generation models, such as Stable Diffusion and DALL-E 3, hold significant potential in content creation and publishing workflows . however, considerable efforts are being made to prevent the generation of harmful content, such abusive, violent, or pornographic material.
Approach:	They propose a chain-of-jailbreak method which decomposes malicious queries into multiple sub-queries and iteratively edits images based on these sub-questions.
Outcome:	The proposed method can bypass safeguards of image generation models for over 60% cases, significantly outperforms other jailbreaking methods (14%)

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Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao, Jen-tse Huang, Youliang Yuan, Pinjia He, Shuai Wang, Zhaopeng Tu

A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)

Challenge:	Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images.
Approach:	They propose a multimodal safety awareness benchmark to evaluate MLLMs across 29 safety scenarios with 1,500 carefully curated image-prompt pairs.
Outcome:	The proposed model is able to identify unsafe content and avoid over-sensitivity that can hinder helpfulness.

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Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, Shuai Ma

Text Generation

Challenge:	Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance.
Approach:	They propose a retrieval and rewriting based method to better initialize unsupervised translation models.
Outcome:	The proposed method improves translation performance by over 4 BLEU scores.

ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)

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Ningning Wang, Guobing Gan, Peng Zhang, Shuai Zhang, Junqiu Wei, Qun Liu, Xin Jiang

Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)

Challenge:	Existing sparse attention methods use fixed patterns to select words without considering similarities between words.
Approach:	They propose a neural clustering method which integrates into the Self-Attention Mechanism in Transformer and integrates it into the target task.
Outcome:	The proposed method outperforms two typical sparse attention methods on translation, text classification, and text matching tasks while having a comparable or even better time and memory efficiency.

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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao

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

Challenge:	Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics .
Approach:	They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function.
Outcome:	The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining.

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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.

Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion (2024.emnlp-main)

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Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia Hu

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

Challenge:	Existing mechanisms compromise ownership rights or raise data privacy concerns . existing mechanisms compromise security of released large language models .
Approach:	They propose a TaylorMLP to preserve the ownership of large language models by transforming the weights of LLMs into Taylor-series parameters instead of releasing original weights .
Outcome:	The proposed model preserves ownership of large language models and prevents their abuse by adjusting the generation speed and causing low-speed token generation.

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao

Code Execution with Pre-trained Language Models (2023.findings-acl)

Challenge:	LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data.
Approach:	They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format.
Outcome:	The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets.

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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

Challenge:	Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution.
Approach:	They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models.
Outcome:	The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation.

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Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li

Challenge:	Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach:	They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome:	The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.

Reinforcement Learning on Pre-Training Data (2026.acl-long)

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Yong Cao, Xianzhi Li, Huiwen Liu, Wen Dai, Shuai Chen, Bin Wang, Min Chen, Daniel Hershcovich

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)

Challenge:	Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task.
Approach:	They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision.
Outcome:	The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods .

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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents (2025.findings-acl)

Challenge:	Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost .
Approach:	They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation .
Outcome:	GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%.

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Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, Hongsheng Li

Challenge:	a new dataset is being developed to improve the capabilities of mobile GUI-control agents.
Approach:	They propose a dataset designed for generalist mobile GUI-control agents . they use screenshots from popular mobile applications to create a detailed GUI-annotated dataset .
Outcome:	The Android Multi-annotation EXpo (AMEX) is a large-scale dataset for generalist mobile GUI-control agents . it includes screenshots from popular mobile applications, which are annotated at multiple levels .

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng

Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (D19-1)

Challenge:	Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios.
Approach:	They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance.
Outcome:	The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures.

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Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, Shuai Ma

A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)

Challenge:	Existing approaches to build initial unsupervised machine translation models with cross-lingual n-gram embeddings are inexplicit and limited.
Approach:	They propose a cross-lingual pre-training method that incorporates cross-linguistic training signals into pre-trained models by randomly choosing source n-grams in the input text stream.
Outcome:	The proposed method significantly improves the performance of unsupervised machine translation.

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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2025.findings-naacl)

Challenge:	Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps.
Approach:	They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement.
Outcome:	The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps.

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Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, Ziwei Liu

UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models (2024.findings-emnlp)

Challenge:	Current large foundational models have demonstrated transformative capabilities, approaching or surpassing human-level performances in many tasks.
Approach:	They propose a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations.
Outcome:	The proposed framework has 50 tasks and more than 10 models to promote transparent and reproducible evaluations.

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Yue Jiang, Qin Chao, Yile Chen, Xiucheng Li, Shuai Liu, Gao Cong

Interleaved Latent Visual Reasoning with Selective Perceptual Modeling (2026.acl-long)

Challenge:	UrbanLLM is a fine-tuned large language model designed to tackle diverse urban problems.
Approach:	They propose a fine-tuned large language model to tackle diverse urban problems . UrbanLLM decomposes urban-related queries into manageable sub-tasks .
Outcome:	The proposed model outperforms existing models in urban planning and management tasks.

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Shuai Dong, Siyuan Wang, Xingyu Liu, Chenglin Li, Haowen Hou, Zhongyu Wei

A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction (2020.acl-main)

Challenge:	Existing approaches to interleaved reasoning are limited by the cost of re-encoding pixel-dense images.
Approach:	They propose a framework that unifies dynamic state evolution with precise perceptual modeling.
Outcome:	The proposed framework outperforms existing approaches on multimodal reasoning benchmarks.

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Shuo Ren, Shujie Liu, Ming Zhou, Shuai Ma

Not All Citations Are Equal:Entropy-Guided Citation Selection for Noise-Resistant Medical LLM (2026.findings-acl)

Challenge:	Recent methods for bilingual lexicon induction are based on unsupervised cross-lingual word embeddings . previous methods only use word-level information, which is limited and inaccurate .
Approach:	They propose a graph-based approach to induce bilingual lexicons in a coarse-to-fine way . they use word cliques from graphs and aligned clique-level words to find initial translation solution .
Outcome:	The proposed method improves the performance of bilingual lexicon induction compared with previous methods.

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Minyu Gao, Hanlin Xiao, Ruoyu Wang, Shuai Yang, YeXuan Zhang, Xin Wu, Xingyu Liu

ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)

Challenge:	Large language models have demonstrated extensive potential in medical applications . however, their practical deployment in healthcare faces significant challenges .
Approach:	They propose a training-free multi-turn reasoning framework and a post-training methodology that provides external knowledge support for large language models.
Outcome:	The proposed framework elicits internal thought, external thought, and fusion thought, with an entropy-based reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations.

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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead.
Approach:	ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.
Outcome:	ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

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Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

Learning Retrieval Augmentation for Personalized Dialogue Generation (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

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Qiushi Huang, Shuai Fu, Xubo Liu, Wenwu Wang, Tom Ko, Yu Zhang, Lilian Tang

Dialogue Systems & Conversational Agents

Challenge:	Personalized dialogue generation is a popular approach for conversational AI applications . however, persona profiles may not provide comprehensive descriptions of the persona .
Approach:	They propose a method that leverages persona profiles and dialogue context to generate personalized dialogues by leveraging personas and persona profile.
Outcome:	The proposed method outperforms baselines on the CONVAI2 dataset . it is expected to generate personalized dialogues based on persona profiles and dialogue context .

Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)

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Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, Huaiyu Wan

Green & Sustainable NLP

Challenge:	Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient.
Approach:	They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training.
Outcome:	The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways.

Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon (2021.findings-acl)

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Shuai Wang, Guangyi Lv, Sahisnu Mazumder, Bing Liu

Sentiment Analysis

Challenge:	Existing studies on sentiment lexicons have focused on domain-dependent sentiment words.
Approach:	They propose a graph-based technique to detect and correct domain-dependent sentiment words . they propose to use a sentiment lexicon to classify sentiments in a lexical-based classifier .
Outcome:	The proposed method is effective on multiple datasets from different domains.

Multi-level Relevance Document Identifier Learning for Generative Retrieval (2025.acl-long)

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Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Shuai Zhang, Xiang Li, Fuzhen Zhuang, Wei Lin, Zhao Zhang

Information Retrieval

Challenge:	Existing methods generate DocIDs based on textual content, which may result in weak semantic connections for similar documents due to variations in expression.
Approach:	They propose a new retrieval paradigm that generates unique document identifiers . they propose to use queries as a bridge to connect documents with varying relevance levels .
Outcome:	The proposed approach outperforms existing methods on multilingual e-commerce search datasets.

Prompt Optimization for Relation Extraction using Reinforcement Learning (2026.findings-acl)

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Ying Liu, Dong Shuai, Cui Zibo, TengQi Ye, Gang Wu

SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021.acl-long)

Challenge:	Existing prompt-based methods rely heavily on large-scale annotated datasets limiting their applicability in domain-specific and low-resource scenarios.
Approach:	They propose a reinforcement learning-based automated prompt optimization framework for domain relation extraction that optimizes prompt quality through interaction with a black-box LLM.
Outcome:	The proposed framework outperforms existing prompt-based methods and supervised baselines on multiple extraction datasets across medical, financial, legal, and news domains.

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Shuo Ren, Long Zhou, Shujie Liu, Furu Wei, Ming Zhou, Shuai Ma

Split and Merge: Aligning Position Biases in LLM-based Evaluators (2024.emnlp-main)

Challenge:	Using pre-training methods for NMT models is difficult because of the cross-attention module . cross-linguistic embeddings are not used to pretrain a decoder .
Approach:	They propose a semantic interface between pre-trained encoder and pre-train decoder to improve NMT performance.
Outcome:	The proposed method improves on translation and unsupervised translation tasks.

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Zongjie Li, Chaozheng Wang, Pingchuan Ma, Daoyuan Wu, Shuai Wang, Cuiyun Gao, Yang Liu

A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining (2020.findings-emnlp)

Challenge:	Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems.
Approach:	They propose an alignment-based system that calibrates position bias in a lightweight yet effective manner by taking into account both length and semantics and combining them into a single prompt.
Outcome:	Extensive experiments with six LLMs on 11,520 answer pairs show that PORTIA significantly improves consistency and consistency rates with humans.

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Jiahua Chen, Shuai Wang, Sahisnu Mazumder, Bing Liu

Information Extraction & Text Mining

Challenge:	Existing methods to classify and resolve coreferences in opinionated reviews require domain-specific knowledge.
Approach:	They propose to automatically mine domain-specific knowledge for opinionated reviews by combining it with commonsense knowledge.
Outcome:	The proposed approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage commonsense knowledge for the task.

Style Transfer with Multi-iteration Preference Optimization (2025.naacl-long)

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Shuai Liu, Jonathan May

Text Generation

Challenge:	Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization.
Approach:	They propose to use a pseudo-parallel data generation method and a dynamic weighted reward aggregation method to improve upon established preference optimization techniques.
Outcome:	The proposed model outperforms existing models on two commonly used text style transfer datasets and is compared with state-of-the-art models.

Triangular Architecture for Rare Language Translation (P18-1)

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Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)

Challenge:	Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language.
Approach:	They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs.
Outcome:	Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods.

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Meihan Tong, Shuai Wang, Bin Xu, Yixin Cao, Minghui Liu, Lei Hou, Juanzi Li

Low-Resource NLP

Challenge:	Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario.
Approach:	They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) .
Outcome:	The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks.

Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks (2025.findings-emnlp)

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Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao, Hou-I Liu, Ya-Hsin Yeh, Yu-Kai Chan, Wen-Sheng Lien, Po-Yen Kuo, Philip S. Yu, Hong-Han Shuai

Robustness in NLP

Challenge:	Large language models (LLMs) are used to review academic papers, but are susceptible to textual adversarial attacks.
Approach:	They evaluate the robustness of large language models as automated reviewers in the presence of adversarial attacks.
Outcome:	The proposed model is robust against textual adversarial attacks, the authors argue . their findings highlight the importance of addressing adversarials to ensure integrity of scholarly communication.

The Million Authors Corpus: A Cross-Lingual and Cross-Domain Wikipedia Dataset for Authorship Verification (2025.findings-acl)

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Abraham Israeli, Shuai Liu, Jonathan May, David Jurgens

ImgTrojan: Jailbreaking Vision-Language Models with ONE Image (2025.naacl-long)

Challenge:	Authorship verification (AV) is a crucial task for identity verification, accountlinking, historical linguistics, and AI-generated text identification.
Approach:	They propose to use Wikipedia's Million Authors Corpus to examine authorship verification models on a broad scale.
Outcome:	The proposed dataset includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors.

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Xijia Tao, Shuai Zhong, Lei Li, Qi Liu, Lingpeng Kong

MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)

Challenge:	Existing studies on the safety of large language models (LLMs) with human values have focused on the integration of multi-modal user input into these models.
Approach:	They propose a method to bypass safety constraints of large language models by using poisoned images instead of original textual captions.
Outcome:	The proposed attack bypasses safety constraints of large language models (VLMs) by replacing the original textual captions with malicious jailbreak prompts.

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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu

Reasoning

Challenge:	Existing systems focus primarily on assessment rather than treatment planning.
Approach:	They propose a framework that structures LLM reasoning to align with real-life workflows.
Outcome:	The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality.

Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)

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Hao Wang, Shuai Wang, Sahisnu Mazumder, Bing Liu, Yan Yang, Tianrui Li

Information Retrieval

Challenge:	Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task.
Approach:	They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones.
Outcome:	The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions.

Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)

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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen

Can Language Model Moderators Improve the Health of Online Discourse? (2024.naacl-long)

Challenge:	Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem.
Approach:	They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces.
Outcome:	The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems.

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Hyundong Cho, Shuai Liu, Taiwei Shi, Darpan Jain, Basem Rizk, Yuyang Huang, Zixun Lu, Nuan Wen, Jonathan Gratch, Emilio Ferrara, Jonathan May