Papers by Chang Xu

Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)

Challenge:	a vision-language benchmark for human activity planning is designed for humans . the task is easy for humans, but challenging for SOTA deep learning models .
Approach:	They propose a vision-language benchmark for human activity planning that extends Charades with intents and builds on a multi-choice question test set.
Outcome:	The proposed benchmark evaluates the ability of systems to anticipate and plan human actions in a multimodal visionlanguage setting.

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Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, Saloni Potdar

V-ALPHASOCIAL: Benchmark and Self-Reflective Chain-of-Thought Generation for Visual Social Commonsense Reasoning (2025.findings-acl)

Challenge:	Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph.
Approach:	They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once.
Outcome:	The proposed approach can perform state-of-the-art on the benchmark ACE 2005.

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Zongyu Lin, Zhikun Xu, Xiaohan Song, Yixin Wan, Xingcheng Yao, Tsung-Han Lin, Selina Song, Pranav Subbaraman, Ben Zhou, Kai-Wei Chang, Yizhou Sun

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

Challenge:	Social commonsense reasoning is a multimodal task that requires both textual and visual cues.
Approach:	They propose a method that integrates visual cues into social commonsense reasoning tasks.
Outcome:	The proposed method improves social commonsense reasoning on a multimodal foundation model.

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

Locating and Extracting Relational Concepts in Large Language Models (2024.findings-acl)

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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Zijian Wang, Britney Whyte, Chang Xu

ThoughtProbe: Classifier-Guided LLM Thought Space Exploration via Probing Representations (2025.emnlp-main)

Challenge:	Existing knowledge recall models lack interpretability for relational concepts . a hidden state expresses causal effects of relational concept in input prompts .
Approach:	They propose to use causal mediation analysis to find hidden states that express relational concepts in LLMs.
Outcome:	The proposed representations exhibit high credibility and can be flexibly transplanted into other recall processes.

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Zijian Wang, Chang Xu

Information Retrieval

Challenge:	Unlike previous works that manipulate representations to steer LLM generation, ThoughtProbe harnesses them as discriminative signals to guide the tree-structured response space exploration.
Approach:	They propose a tree-structured inference-time framework that leverages the hidden reasoning features of Large Language Models to improve their reasoning performance.
Outcome:	The proposed framework improves reasoning performance across multiple arithmetic reasoning benchmarks and covers valid reasoning chains and identifies optimal answers.

Double Graph Based Reasoning for Document-level Relation Extraction (2020.emnlp-main)

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Shuang Zeng, Runxin Xu, Baobao Chang, Lei Li

Information Extraction & Text Mining

Challenge:	Existing methods for document-level relation extraction fail to recognize relations between entities across sentences.
Approach:	They propose a method to recognize relations for long paragraphs by a Graph Aggregation-and-Inference Network (GAIN) they propose to use a heterogeneous mention-level graph and an entity-level EG graph to analyze the relationships.
Outcome:	The proposed method achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art.

Towards Fine-grained Text Sentiment Transfer (P19-1)

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Fuli Luo, Peng Li, Pengcheng Yang, Jie Zhou, Yutong Tan, Baobao Chang, Zhifang Sui, Xu Sun

Sentiment Analysis

Challenge:	Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm.
Approach:	They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content.
Outcome:	The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation.

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (2021.emnlp-main)

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Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, Fei Huang

Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)

Challenge:	Recent pretrained language models extend from millions to billions of parameters.
Approach:	They propose a technique which forwards on a whole network while backwarding on resetting the gradients of the non-child network during the backward process.
Outcome:	The proposed technique outperforms the vanilla fine-tuning technique on various downstream tasks and can achieve better generalization performance by large margins.

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Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, Baobao Chang

MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025.acl-long)

Challenge:	In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters.
Approach:	They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update.
Outcome:	The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects.

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Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou

Information Retrieval

Challenge:	Existing RAG systems struggle with the quality of retrieval documents, causing performance degradation and reducing performance.
Approach:	They propose a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents.
Outcome:	The proposed framework outperforms existing RAG frameworks in QA benchmarks and shows superior answer consistency and answer accuracy over baseline methods.

ADAM: Dense Retrieval Distillation with Adaptive Dark Examples (2024.findings-acl)

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Chongyang Tao, Chang Liu, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, Daxin Jiang

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

Challenge:	Existing methods to retrieve data from multiple encoders are too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student.
Approach:	They propose a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples.
Outcome:	The proposed framework can better transfer the dark knowledge held in the teacher with adaptive dark examples.

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

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

Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)

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Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, Yizhou Sun

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)

Challenge:	Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge.
Approach:	They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively.
Outcome:	The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively.

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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang

S4-Tuning: A Simple Cross-lingual Sub-network Tuning Method (2022.acl-short)

Challenge:	Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions.
Approach:	They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm.
Outcome:	The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model.

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Runxin Xu, Fuli Luo, Baobao Chang, Songfang Huang, Fei Huang

Cross-Lingual Transfer

Challenge:	Existing multilingual pre-trained language models allow to adapt to target languages with only few labeled examples.
Approach:	They propose a simple cross-lingual sub-network tuning method that detects the most essential sub-netzwork for each target language and updates it during fine-tuning.
Outcome:	The proposed method improves on three multi-lingual tasks involving 37 different languages.

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

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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

McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models (2025.findings-acl)

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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Tian Lan, Xiangdong Su, Xu Liu, Ruirui Wang, Ke Chang, Jiang Li, Guanglai Gao

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

Challenge:	Existing datasets on bias evaluation for large language models focus on English and North American culture and are limited to one task.
Approach:	They propose to evaluate Chinese language models' biases from multiple perspectives using a multi-task Chinese Bias Evaluation Benchmark.
Outcome:	The proposed model covers 12, 82 subcategories and 5 evaluation tasks covering a wide range of categories and content diversity.

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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%.

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

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

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

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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

When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation (2026.eacl-short)

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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Xunyi Jiang, Dingyi Chang, Julian McAuley, Xin Xu

Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)

Challenge:	Existing studies on LLM factuality evaluation have not investigated the reliability of static evaluation benchmarks.
Approach:	They examine five popular factuality benchmarks and eight LLMs released over different years to assess their reliability.
Outcome:	The proposed method compared five popular factuality benchmarks and eight LLMs released over different years.

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Fuli Luo, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, Xu Sun

CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V (2025.findings-naacl)

Challenge:	Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses)
Approach:	They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses.
Outcome:	The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation.

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Siyu Xu, Yunke Wang, Daochang Liu, Bo Du, Chang Xu

Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)

Challenge:	Recent advances in generative AI have suggested that by taking visual prompts, GPT-4V can demonstrate significant proficiency in visual recognition tasks.
Approach:	They propose a collage prompting task that collages multiple images into a single visual prompt and makes GPT-4V perform visual recognition on several images simultaneously.
Outcome:	The proposed task reduces the cost associated with GPT-4V's visual recognition . the proposed task group images of the same category together leads to better visual recognition results .

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models .
Approach:	They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision.
Outcome:	The proposed framework improves LLM reasoning without supervision without external supervision.

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Zhirong Chen, Kaiyan Chang, Zhuolin Li, Cangyuan Li, Xinyang He, Chujie Chen, Mengdi Wang, Haobo Xu, Yinhe Han, Huawei Li, Ying Wang

Green & Sustainable NLP

Challenge:	Existing approaches to optimize Register-Transfer Level (RTL) code fail to simultaneously optimize functional correctness and hardware efficiency metrics such as Power, Performance, and Area (PPA).
Approach:	They propose a hierarchical reward based reinforcement learning framework that integrates direct feedback from EDA simulators and synthesis tools into a reward mechanism.
Outcome:	The proposed framework integrates direct feedback from EDA simulators and synthesis tools into a hierarchical reward based reinforcement learning framework.

PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)

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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen

Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning (2021.findings-acl)

Challenge:	Existing evaluations of phone recognition systems only measure surface-level transcription accuracy.
Approach:	They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes.
Outcome:	The proposed system outperforms LALMs in clinical, educational, and multilingual settings.

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Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein, Trevor Cohn

AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)

Challenge:	Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, they are also vulnerable to training attacks.
Approach:	They propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into a training set of a system trained using back-translation.
Outcome:	The proposed attack is based on two methods that can be used to craft poisoned examples.

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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai

Cognitive Modeling

Challenge:	Lossless compression has made significant advancements in Genomics Data storage, sharing and management.
Approach:	They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker.
Outcome:	The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface.

Weight Perturbation as Defense against Adversarial Word Substitutions (2022.findings-emnlp)

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Jianhan Xu, Linyang Li, Jiping Zhang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang

Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)

Challenge:	Existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications.
Approach:	They propose to perform weight perturbations in the parameter space rather than the input feature space to improve adversarial robustness of NLP models.
Outcome:	The proposed method improves adversarial robustness of models by performing weight perturbations in the parameter space rather than the input feature space.

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Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou

Machine Translation

Challenge:	Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available.
Approach:	They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities .
Outcome:	The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text.

Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker (2021.acl-long)

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Runxin Xu, Tianyu Liu, Lei Li, Baobao Chang

Information Extraction & Text Mining

Challenge:	Existing methods for document-level event extraction are not effective due to two challenges . existing methods fail to extract events whose arguments spread in multiple sentences .
Approach:	They propose a document-level event extraction model with a tracker to capture interdependency among the extracted events.
Outcome:	The proposed model outperforms existing models on a large-scale dataset by 2.8 F1 . it extracts multiple correlated events and event arguments that scatter across the document .

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)

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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui

ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)

Challenge:	Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query.
Approach:	They propose a task where a textual premise is the background presumption on each source image.
Outcome:	The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories.

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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi

Challenge:	LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints.
Approach:	They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks.
Outcome:	The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration.

Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study (2025.emnlp-main)

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Yizheng Sun, Hao Li, Chang Xu, Hongpeng Zhou, Chenghua Lin, Riza Batista-Navarro, Jingyuan Sun

A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction (2022.naacl-main)

Challenge:	Current acceleration evaluations focus on minimal overall performance degradation . however, accelerated models can exhibit significant changes in instance-level predictions .
Approach:	They investigate whether accelerated vision-Language Models can still give the same answers as before . they found that accelerated models changed original answers up to 20% of the time .
Outcome:	The results show that accelerated models changed their original answers up to 20% of the time.

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Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng, Baobao Chang, Zhifang Sui

Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)

Challenge:	Existing studies aim at extracting event arguments from a single sentence . document-level event extraction still remains under-explored .
Approach:	They propose a two-stream abstract meaning representation enhanced extraction model to extract event arguments from an entire document.
Outcome:	The proposed model outperforms state-of-the-art in extracting event arguments from documents by 2.54 F1 and 5.13 F1 on public RAMS and WikiEvents datasets.

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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

Challenge:	Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs.
Approach:	They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides.
Outcome:	The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation.

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Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, Jingren Zhou

Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (P19-1)

Challenge:	Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach:	They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
Outcome:	The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format.

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Chang Xu, Cecile Paris, Surya Nepal, Ross Sparks

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for (dis)agreement detection focus on conversational settings . however, non-dialogic stance-bearing utterances are common in real-world scenarios .
Approach:	They propose a reason comparing network to leverage reason information for stance comparison.
Outcome:	The proposed method outperforms baselines on a well-known stance corpus.

A Survey on In-context Learning (2024.emnlp-main)

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)

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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang

Cross-Target Stance Classification with Self-Attention Networks (P18-2)

Challenge:	despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education.
Approach:	They propose to develop a benchmark specifically tailored for Chinese K-12 education.
Outcome:	EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education.

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Chang Xu, Cécile Paris, Surya Nepal, Ross Sparks

Text Classification

Challenge:	In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target.
Approach:	They propose a neural model that can generalize classifiers between different targets by finding useful information shared between relevant targets.
Outcome:	The proposed model can generalize between relevant targets and find useful information shared between relevant target domains which improves generalization in certain scenarios.

Achilles-Bench: A Challenging Benchmark for Low-Resource Evaluation (2024.findings-acl)

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Yudong Wang, Chang Ma, Qingxiu Dong, Zhifang Sui, Lingpeng Kong, Jingjing Xu

Assessing Social License to Operate from the Public Discourse on Social Media (2020.coling-industry)

Challenge:	Existing low-resource datasets that challenge neural networks cause over-estimated performance, despite promising yet saturated results in high-res settings.
Approach:	They propose a benchmark Achilles-Bench to better evaluate the learning ability of neural networks in low-resource settings.
Outcome:	The proposed benchmarks show that even pre-trained language models show performance drops on NLP tasks.

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Chang Xu, Cecile Paris, Ross Sparks, Surya Nepal, Keith VanderLinden

Challenge:	Social License to Operate (SLO) is the level of support organisations gain from the public.
Approach:	They propose to extract and transform peoples’ stances towards an organisation into SLO levels by performing a chain of three text classification tasks.
Outcome:	The proposed system extracts and transforms peoples’ stances towards an organisation into SLO levels.

Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)

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Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, Nanyun Peng

Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation (2022.acl-short)

Challenge:	Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering.
Approach:	They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT.
Outcome:	The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance.

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Liang Chen, Runxin Xu, Baobao Chang

Machine Translation

Challenge:	Label smoothing and vocabulary sharing are widely used in neural machine translation models, but they can be conflicting and lead to suboptimal performance.
Approach:	They propose a mechanism that masks the soft label probability of source-side words to zero and integrates label smoothing with vocabulary sharing to improve translation quality.
Outcome:	The proposed mechanism improves translation quality and model calibration on bilingual and multilingual datasets, while retaining the original smoothing method.

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)

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Liang Chen, Peiyi Wang, Runxin Xu, Tianyu Liu, Zhifang Sui, Baobao Chang

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)

Challenge:	Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations.
Approach:	They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing.
Outcome:	The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores.

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Chiwei Zhu, Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Zhendong Mao

DISK: Domain-constrained Instance Sketch for Math Word Problem Generation (2022.coling-1)

Challenge:	Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently.
Approach:	They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability.
Outcome:	The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales.

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Tianyang Cao, Shuang Zeng, Xiaodan Xu, Mairgup Mansur, Baobao Chang

Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models (2021.acl-long)

Challenge:	Existing methods for generating MWP text from equations are inflexible and require pre-defined templates.
Approach:	They propose a neural model which generates MWPs from equations by constructing a Quantity Cell Graph from the retrieved MWp instance and reasoning over it.
Outcome:	The proposed model performs impressively on educational MWP set and on human evaluation metrics.

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Tyler Chang, Yifan Xu, Weijian Xu, Zhuowen Tu

An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling (2022.naacl-main)

Challenge:	Recent work has shown that convolutions have been successful in natural language learning.
Approach:	They propose a convolutional approach to construct relative position embeddings in self-attention layers and propose 'compact attention' they propose multiple ways to integrate convolutions into Transformer self- attention.
Outcome:	The proposed composite attention improves performance on multiple downstream tasks, replacing absolute position embeddings, and is more expressive than convolutions in NLP.

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Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao Chang, Zhifang Sui

CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China (2026.acl-long)

Challenge:	Existing approaches to tagging tasks are limited to predefined classes and require large-scale annotated data.
Approach:	They propose an Enhanced Span-based Decomposition method for Few-Shot Sequence Labeling to generalize on emerging, resource-scare domains.
Outcome:	The proposed method achieves state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is more robust in noisy and nested tagging scenarios.

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Bolun Sun, Charles Chang, Yuen Yuen Ang, Ruotong Mu, Yuchen Xu, Zhengxin Zhang, Pingxu Hao

Challenge:	Adaptive policy communication is a theory of governance in large, decentralized organizations where leaders exercise influence rather than precise control by combining clear and ambiguous instructions to calibrate discipline and flexibility.
Approach:	They propose an expert-directed annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling.
Outcome:	The proposed method achieves a Fleiss’ kappa of 0.86 on directive labels, indicating high reliability.

Learning to Control the Fine-grained Sentiment for Story Ending Generation (P19-1)

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Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, Xu Sun

Sentiment Analysis

Challenge:	Existing studies focus on controlling the sentiment of story endings.
Approach:	They propose a generic and novel framework which controls fine-grained sentiment intensity for automatic story ending generation without manually annotating sentiment labels.
Outcome:	The proposed framework can generate story endings which meet the given sentiment intensity better.

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)

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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

Mitigating Legal Hallucinations via Symbolic Constraints and Analogical Precedents (2026.acl-long)

Challenge:	Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase.
Approach:	They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance .
Outcome:	The proposed method outperforms the leading competitors on the GLUE benchmark.

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Zixuan Huang, Yanxiang Ma, Luhan Wang, Yunke Wang, Duo Shi, Chang Xu

Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations (2023.findings-acl)

Challenge:	Existing methods for finetuning and retrieval-augmented generation suffer from hallucination risk and semantic drift.
Approach:	They propose a framework for a dual-retriever based on the legal syllogism and the nature of different legal data.
Outcome:	The proposed framework mitigates hallucinations while improving explainability of legal reasoning.

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Zixuan Ling, Xiaoqing Zheng, Jianhan Xu, Jinshu Lin, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Existing methods to learn models on corpus of pairs of sentences require labor-intensive annotation.
Approach:	They propose to leverage distributed contextual word and phrase representations pre-trained on unlabelled texts to deal with homonymy and polysemy.
Outcome:	The proposed model achieves better accuracy on question-answering and relation extraction tasks.

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

Multilinguality

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)

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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang

Visual Data in NLP

Challenge:	Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.)
Approach:	They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data.
Outcome:	The proposed approach improves model performance even in domain-shifted scenarios.

Mitigating Data Poisoning in Text Classification with Differential Privacy (2021.findings-emnlp)

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Chang Xu, Jun Wang, Francisco Guzmán, Benjamin Rubinstein, Trevor Cohn

Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)

Challenge:	Data poisoning attacks can plant a backdoor in a model by injecting poisoned examples into training data, causing the model to misclassify test instances which include a specific pattern.
Approach:	They propose a generic defence mechanism that makes training robust to poisoning attacks by smoothing the gradient from each training example.
Outcome:	The proposed method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy.

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Lei Zeng, Ruifang He, Haowen Sun, Jing Xu, Chang Liu, Bo Wang

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer.
Approach:	They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer.
Outcome:	The proposed framework achieves competitive results on two benchmacks.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022.findings-acl)

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Jianhan Xu, Cenyuan Zhang, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang

𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)

Challenge:	Existing defense methods improve the adversarial robustness by making models adapt to training set augmented with some adversarials.
Approach:	They propose to introduce a reweighting mechanism to calibrate the training distribution to obtain robust models.
Outcome:	The proposed method minimizes the loss of validation set mixed with clean examples and adversarial ones in an online learning manner.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Jun Liu, Qika Lin, Zhiyong Wu

Green & Sustainable NLP

Challenge:	Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation.
Approach:	They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value.
Outcome:	The proposed decoding strategy outperforms strong baselines in performance and efficiency.

ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)

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Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy .
Approach:	They propose a framework that utilizes simulated dialogue futures to enhance response generation.
Outcome:	The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets.

A Double-Graph Based Framework for Frame Semantic Parsing (2022.naacl-main)

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Ce Zheng, Xudong Chen, Runxin Xu, Baobao Chang