Papers by Lu Xu

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)

Challenge:	Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency.
Approach:	They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective.
Outcome:	The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs.

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Dingyao Yu, Kaitao Song, Peiling Lu, Tianyu He, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian

SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)

Challenge:	MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements.
Approach:	a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests .
Outcome:	the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements.

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Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun

Challenge:	SafeAgent improves agent safety through fully automated synthetic data generation.
Approach:	They propose a framework that improves agent safety through fully automated synthetic data generation.
Outcome:	The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task.

TrInk: Ink Generation with Transformer Network (2025.emnlp-main)

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu

CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)

Challenge:	Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples.
Approach:	They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies.
Outcome:	The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models.

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Hanchong Zhang, Ruisheng Cao, Hongshen Xu, Lu Chen, Kai Yu

Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation (2026.acl-long)

Challenge:	Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks.
Approach:	They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain.
Outcome:	The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs.

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Dianyun Wang, Qingsen Ma, Yuhu Shang, Zhifeng Lu, Zhenbo Xu, Lechen Ning, Huijia Wu, Zhaofeng He

Explainability & Interpretability in NLP

Challenge:	Prior work has shown that safety behaviors are governed by low-rank structures . Low-Rank Adaptation (LoRA) consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks .
Approach:	They propose a safety alignment system that disentangles safety-relevant directions into monosemantic features and constructs an interpretable safety subspace from SAE directions.
Outcome:	Empirically, the proposed model achieves 99.6% safety rates across multiple model families and scales . low-rank Adaptation consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks compared with previous methods .

Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)

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Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang

Challenge:	GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs.
Approach:	They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training.
Outcome:	The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o .

CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks (2026.acl-long)

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Xu Zhang, Hao Li, Zhichao Lu

Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios (2025.findings-naacl)

Challenge:	Existing methods for MLLMs are weak on explicit attacks, but weak on implicit ones.
Approach:	They propose an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains.
Outcome:	The proposed method outperforms existing methods in implicit and explicit attacks while maintaining high utility.

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Jiwei Tang, Jin Xu, Tingwei Lu, Zhicheng Zhang, YimingZhao YimingZhao, LinHai LinHai, Hai-Tao Zheng

DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)

Challenge:	Long prompts contain redundant information and are sensitive to the position of key information in long context scenarios.
Approach:	They propose a training-free prompt compression framework that retains key information at token level while removing distracting tokens.
Outcome:	The proposed framework outperforms existing methods on long context benchmarks.

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Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi

MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs (2026.findings-acl)

Challenge:	Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks.
Approach:	They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks.
Outcome:	The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach.

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Dezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han

Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)

Challenge:	Existing models struggle to detect elaborately disguised malicious URLs, despite their ability to process malicious URL's.
Approach:	They propose a benchmark to evaluate LLMs’ vulnerabilities to malicious URLs and a lightweight defense module to mitigate the vulnerability.
Outcome:	The proposed framework analyzes 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites.

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Zhicheng Lin, HeGang Chen, Yuyin Lu, Yanghui Rao, Hao Xu, Hanjiang Lai

Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)

Challenge:	Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics.
Approach:	They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents.
Outcome:	The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy.

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Wenda Xu, Yi-Lin Tuan, Yujie Lu, Michael Saxon, Lei Li, William Yang Wang

A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)

Challenge:	Existing learning metrics are limited to tasks where large human ratings are available.
Approach:	They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation.
Outcome:	The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation.

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Xinpeng Liu, Bing Xu, Muyun Yang, Hailong Cao, Conghui Zhu, Tiejun Zhao, Wenpeng Lu

Low-Resource NLP

Challenge:	Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs.
Approach:	They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation .
Outcome:	The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets.

Multilingual Brain Surgeon: Large Language Models Can Be Compressed Leaving No Language behind (2024.lrec-main)

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Hongchuan Zeng, Hongshen Xu, Lu Chen, Kai Yu

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)

Challenge:	Existing methods for MC focus on quantization and network pruning.
Approach:	They propose a calibration method that samples calibration data from various languages proportionally to the language distribution of the model training datasets.
Outcome:	The proposed method improves the performance of existing English-centric compression methods on the BLOOM multilingual LLM.

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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang

TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)

Challenge:	Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed .
Approach:	They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management.
Outcome:	The proposed system automates human management by using a collaborative multi-agent system.

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Jiuding Yang, Shengyao Lu, Weidong Guo, Xiangyang Li, Kaitong Yang, Yu Xu, Di Niu

Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks.
Approach:	They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs.
Outcome:	The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks.

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Jieyu Li, Lu Chen, Ruisheng Cao, Su Zhu, Hongshen Xu, Zhi Chen, Hanchong Zhang, Kai Yu

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated.
Approach:	They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
Outcome:	The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Hongbo Wang, LiMingDa LiMingDa, Junyu Lu, Hebin Xia, Liang Yang, Bo Xu, Ruizhu Liu, Hongfei Lin

Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)

Challenge:	Patronizing and condescending language is an essential branch of toxic language . pre-trained language models perform poorly in detecting PCL due to its implicit toxicity traits .
Approach:	They propose a novel LLM benchmark for patronizing and condescending language . they use a dataset to analyze the toxicity of patronizing condescending languages .
Outcome:	The proposed model can detect patronizing and condescending language (PCL) the model can be used to analyze the toxicity of the language and to improve the detection.

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Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou

Demystifying Small Language Models for Edge Deployment (2025.acl-long)

Challenge:	Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them .
Approach:	They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them .
Outcome:	The proposed method outperforms existing methods on long- and short-text datasets.

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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu

DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)

Challenge:	Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things.
Approach:	They propose to use SLMs to build and optimize a set of small language models that are publicly accessible.
Outcome:	The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential.

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Haoyuan Ma, Yongliang Shen, Hengwei Liu, Wenqi Zhang, Haolei Xu, Qiuying Peng, Jun Wang, Weiming Lu

EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)

Challenge:	Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries.
Approach:	a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas .
Outcome:	a new framework outperforms existing text-to-SQL systems by outperforming existing systems.

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Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou

Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)

Challenge:	Existing methods for data augmentation do not fully exploit the potential of DA in NLP.
Approach:	They propose an easy and plug-in framework for data augmentation to support effective text classification.
Outcome:	The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks.

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Wei Li, Zhen Huang, Xinmei Tian, Le Lu, Houqiang Li, Xu Shen, Jieping Ye

Explainability & Interpretability in NLP

Challenge:	Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms.
Approach:	They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text.
Outcome:	The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements.

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)

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Jizhan Fang, Tianhe Lu, Yunzhi Yao, Ziyan Jiang, Xin Xu, Huajun Chen, Ningyu Zhang

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)

Challenge:	CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models.
Approach:	They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models.
Outcome:	The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show .

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, Sharifah Mahani Aljunied

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED.
Approach:	They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED.
Outcome:	The proposed dataset improves on the existing DocRED dataset by 13 F1 points.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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

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

Representation Learning

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

Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)

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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen

Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)

Challenge:	Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions.
Approach:	They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification.
Outcome:	The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% .

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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu

Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)

Challenge:	Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects.
Approach:	They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt.
Outcome:	The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks.

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Wei Li, Zhen Huang, Houqiang Li, Le Lu, Yang Lu, Xinmei Tian, Xu Shen, Jieping Ye

A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)

Challenge:	LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs.
Approach:	They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them.
Outcome:	The proposed method reduces hallucinations by reducing false activation and enhancing correct ones.

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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong

ECHA: Jailbreaking LVLMs via the Mismatch between Implicit Semantic Reconstruction and Explicit Safety Alignment (2026.findings-acl)

Challenge:	Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices.
Approach:	They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features.
Outcome:	The proposed framework aims to improve the performance of large language models on various tasks.

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Chenxing Xu, Junyong Jiang, Zehu Zhang, Lu Dong

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)

Challenge:	Existing safety guardrails fail to intercept latent intent, whereas LVLMs can implicitly synthesize holistic malicious semantics from fragmented visual cues.
Approach:	They propose an Emoji Chain Hinting Attack (ECHA) framework that decouples sensitive concepts into semantically related emoji chains and structural text masks.
Outcome:	The proposed framework outperforms existing baselines and bypasses safety guardrails in over 81% of instances with a single attempt.

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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

TheoremQA: A Theorem-driven Question Answering Dataset (2023.emnlp-main)

Challenge:	i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data.
Approach:	They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data.
Outcome:	i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks.

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Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, Tony Xia

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

Challenge:	Recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy.
Approach:	They propose to use theorem-driven question-answering dataset to evaluate AI models' ability to apply theoretic concepts to solving challenging science problems.
Outcome:	TheoremQA is curated by domain experts and contains 800 high-quality questions covering 350 theoremics from Math, Physics, EE&CS, and Finance.

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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

StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)

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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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)

Challenge:	Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities.
Approach:	They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework.
Outcome:	The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge.

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Zihan Zhao, Lu Chen, Ruisheng Cao, Hongshen Xu, Xingyu Chen, Kai Yu

Machine Reading Comprehension

Challenge:	Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases.
Approach:	They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process.
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing.

Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)

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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun

Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)

Challenge:	Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge.
Approach:	They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.
Outcome:	The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.

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Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)

Challenge:	Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Approach:	They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons.
Outcome:	The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process.

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Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

Challenge:	Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels .
Approach:	They propose a new architecture which processes schemas at abstract and semantic levels.
Outcome:	The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema .

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

ATGL: An Adaptive-Threshold Global Loss for Document-level Relation Extraction (2026.acl-long)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Huangming Xu, Fu Zhang, Zhixuan Yang, Lu Zhang, Jingwei Cheng

ParaCook: On Time-Efficient Planning for Multi-Agent Systems (2026.findings-acl)

Challenge:	Document-level relation extraction (DocRE) aims to determine which relations hold between a given entity pair in a document.
Approach:	They propose a document-level relation extraction paradigm that decouples existing losses into independent positive and negative losses, which interact solely with a shared threshold.
Outcome:	The proposed model outperforms existing models on four datasets and achieves state-of-the-art results.

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Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang

Green & Sustainable NLP

Challenge:	Existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations.
Approach:	They propose a framework for large language models that allows agents to plan long-horizon tasks in a scalable way.
Outcome:	The proposed framework is based on the Overcooked game and can be used to evaluate time efficiency-aware multi-agent planning.

MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)

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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)

Challenge:	Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance.
Approach:	They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability.
Outcome:	Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks.

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Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, Hongfei Lin

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)

Challenge:	Existing studies focus on evaluating large language models' ability to handle disagreement cases.
Approach:	They evaluate the performance of large language models in detecting offensive language at varying levels of agreement.
Outcome:	The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training.

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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation.
Approach:	They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows.
Outcome:	The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases.

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)

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Zhifeng Hao, Zhongjie Chen, Junhao Lu, Shengyin Yu, Guimin Hu, Keli Zhang, Ruichu Cai, Boyan Xu

Information Retrieval

Challenge:	Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning.
Approach:	They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI.
Outcome:	The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy.

A Large Scale Speech Sentiment Corpus (2020.lrec-1)

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Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, James Fan

NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)

Challenge:	Existing corpus for sentiment analysis uses text inputs, but voice inputs are becoming more important as smart assistants and mobile voice control become more prevalent.
Approach:	They propose to extend the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment.
Outcome:	The proposed corpus contains 49500 labeled speech segments covering 140 hours of audio.

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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

Challenge:	Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas.
Approach:	They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights.
Outcome:	The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.

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Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

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Nayu Liu, Junnan Zhu, Yiming Ma, Zhicong Lu, Wenlei Xu, Yong Yang, Jiang Zhong, Kaiwen Wei

XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)

Challenge:	Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge.
Approach:	They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information.
Outcome:	The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights.

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Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei

LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)

Challenge:	Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization.
Approach:	They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding.
Outcome:	The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks.

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Hongyun Zhou, Xiangyu Lu, Wang Xu, Conghui Zhu, Tiejun Zhao, Muyun Yang

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters.
Approach:	They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA.
Outcome:	The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average.

SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)

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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen

Speech & Audio in NLP

Challenge:	a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens .
Approach:	They propose a timbre-controllable, end-to-end voice interaction system with single-stage training.
Outcome:	The proposed system outperforms previous models on 4 GPUs with limited data.

Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)

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Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng-Ann Heng, Wai Lam

A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood.
Approach:	They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs.
Outcome:	The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks.

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Ye Tao, Chaofeng Lu, Meng Liu, Kai Xu, Tianyu Liu, Yunlong Tian, Yongjie Du

Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)

Challenge:	Existing methods to generate high-quality speech with limited target speaker corpus require extensive training data.
Approach:	They propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of synthesized speech is not significantly degraded.
Outcome:	The proposed method significantly reduces training costs while maintaining the naturalness of synthesized speech.

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Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, Zenglin Xu

Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)

Challenge:	Existing models for response selection do not perform well when there are many candidate responses.
Approach:	They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response .
Outcome:	The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks.

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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)

Challenge:	Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks.
Approach:	They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison.
Outcome:	The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research.

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Ruikang Liu, Haoli Bai, Haokun Lin, Yuening Li, Han Gao, Zhengzhuo Xu, Lu Hou, Jun Yao, Chun Yuan

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

Challenge:	Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation.
Approach:	They propose to generate the KV cache of pivot tokens losslessly from the full-precision model.
Outcome:	The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead.

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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)

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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)

Challenge:	Recent code large language models have demonstrated impressive performance on code-related tasks.
Approach:	They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations .
Outcome:	The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models .

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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu

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

Challenge:	Continual learning (CL) is crucial for large language models without costly retraining.
Approach:	They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer.
Outcome:	The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer.

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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%.

LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)

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Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Question Answering

Challenge:	Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications.
Approach:	They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks.
Outcome:	The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings.

ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought (2023.findings-emnlp)

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Hanchong Zhang, Ruisheng Cao, Lu Chen, Hongshen Xu, Kai Yu

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	Recent studies have focused on the development of semantic parsers within the framework of cross-domain analysis.
Approach:	They propose a method to generate auto-CoT exemplars using ACT-SQL and extend it to multi-turn text-to-Sql tasks.
Outcome:	The proposed method achieves SOTA performance on the Spider dev set among existing in-context learning approaches.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries (2025.findings-acl)

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Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang

RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)

Challenge:	Current dense retrieval methods compute similarities between dense vectors but overlook the real query intents.
Approach:	They propose a neuro-symbolic information retrieval method that leverages first-order logic to optimize the embeddings of naive natural language by considering the logical consistency between queries and documents.
Outcome:	The proposed method outperforms existing methods on negative-constraint queries under zero-shot and low-resource retrieval tasks.

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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang

SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)

Challenge:	Existing benchmarks for large language models focus on simple, flat table structures.
Approach:	They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.
Outcome:	The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.

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Wazir Ali, Junyu Lu, Zenglin Xu

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)

Challenge:	Named entity recognition is an essential lower-level task in natural language processing (NLP).
Approach:	They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines.
Outcome:	The proposed dataset is likely to be a significant resource for statistical Sindhi language processing.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng

Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)

Challenge:	Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage.
Approach:	They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores.
Outcome:	The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated.

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Lu Xu, Hao Li, Wei Lu, Lidong Bing

Sentiment Analysis

Challenge:	Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Approach:	They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach.
Outcome:	The proposed model improves performance on multiple datasets and compares with existing models.

Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)

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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance.
Approach:	They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior.
Outcome:	The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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Sirui Chen, Mengying Xu, Kun Wang, Xingyu Zeng, Rui Zhao, Shengjie Zhao, Chaochao Lu

Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)

Challenge:	Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement.
Approach:	They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines.
Outcome:	The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels.

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Fei Yuan, Yinquan Lu, Wenhao Zhu, Lingpeng Kong, Lei Li, Yu Qiao, Jingjing Xu

Multilinguality

Challenge:	Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models.
Approach:	They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages .
Outcome:	The proposed model outperforms existing models in OPUS and is faster than existing models.

OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)

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Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence Moss

MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026.acl-long)

Challenge:	Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets.
Approach:	They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations.
Outcome:	The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics.

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Yang Shi, Yifeng Xie, Minzhe Guo, Liangsi Lu, Mingxuan Huang, Jingchao Wang, Zhihong Zhu, Boyan Xu, Zhiqi Huang

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

Challenge:	Recent advances in vision-language models have improved performance in multi-modal learning.
Approach:	They propose a multi-modal benchmark that embeds a single coherent reasoning error in 1997 samples.
Outcome:	The proposed benchmark is based on a set of 1997 samples embedding a single coherent reasoning error.

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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu

Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)

Challenge:	With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach:	They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome:	The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.

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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo

Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking? (2025.findings-acl)

Challenge:	Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification .
Approach:	They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning.
Outcome:	The proposed method significantly improves reasoning capabilities of Large Language Models.

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Chengda Lu, Xiaoyu Fan, Yu Huang, Rongwu Xu, Jijie Li, Wei Xu

Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)

Challenge:	Existing jailbreak attacks fail against reasoning models enhanced by Chain-of-Thought (CoT) reasoning.
Approach:	They propose a jailbreak method that uses Chain-of-Thought reasoning to reduce harmfulness from jailbreaking.
Outcome:	The proposed jailbreak method performs well against open AI models and deepseek-R1 reasoning models.

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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

Challenge:	Current diffusion models do not cover recent models, thus we curate three test sets for evaluation.
Approach:	They propose a human-calibrated measure of variability in a set of images bootstrapped from existing image-pair perceptual distances.
Outcome:	The proposed model outperforms nine baselines by 18 points in accuracy and matches graded human judgements 78% of the time.

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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

Challenge:	Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach:	They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome:	The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.

LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)

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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)

Challenge:	Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks.
Approach:	They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework.
Outcome:	The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks.

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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

Challenge:	Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces.
Approach:	They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings.
Outcome:	The citycube benchmark examines the performance of current vision-language models in urban environments.

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, Lu Hou

Visual Data in NLP

Challenge:	Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats .
Approach:	They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect .
Outcome:	The proposed benchmarks show that video large language models exhibit poor temporal perception ability.

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Yuhan Liu, Cong Xu, Lu Liu, Yihua Wang, Feiyu Chen, Qi Jia, Yaqian Zhao, Zhichun Wang, Xiang Li

WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)

Challenge:	Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties.
Approach:	They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning.
Outcome:	The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation.

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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li

Challenge:	Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems.
Approach:	They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success.
Outcome:	Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models.

Sampling Better Negatives for Distantly Supervised Named Entity Recognition (2023.findings-acl)

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Lu Xu, Lidong Bing, Wei Lu

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	Existing supervised named entity recognition approaches rely on human annotations.
Approach:	They propose a method to select negative samples with high similarities with positive samples . they propose to use automatically labeled training data instead of human annotations .
Outcome:	The proposed method achieves consistent performance improvements on four distantly supervised NER datasets.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

PreAct: Prediction Enhances Agent’s Planning Ability (2025.coling-main)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, Yejie Wang, Keqing He, Weiran Xu

Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic (2025.emnlp-main)

Challenge:	Existing methods to analyze Markov decision processes (MDPs) are based on chain-of-thought (COT) and historical thought, action, and observation.
Approach:	They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions.
Outcome:	The proposed model outperforms the ReAct method in completing complex tasks and is more efficient when paired with other memory or selection strategy techniques.

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Yang Yan, Yu Lu, Renjun Xu, Zhenzhong Lan

LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)

Challenge:	Large language models (LLMs) excel at complex math but fail on basic addition, raising the question of whether they grasp rules or are merely reproducing patterns.
Approach:	They systematically probe LLMs’ understanding of two-integer addition by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping and consistent accuracy scaling with operand length.
Outcome:	The proposed models achieve high numeric accuracy but fail basic addition tasks.

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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng

DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)

Challenge:	E-commerce search relevance is a critical component of retrieval systems.
Approach:	They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies.
Outcome:	The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain.

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Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

Challenge:	Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly.
Approach:	They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop.
Outcome:	The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop.

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Information Retrieval

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)

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Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao, Xiliang Lu

Better Feature Integration for Named Entity Recognition (2021.naacl-main)

Challenge:	Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction.
Approach:	They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations.
Outcome:	The proposed approach corrects the overfitted performance caused by prompt bias and significantly improves prompt retrieval capability.

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Lu Xu, Zhanming Jie, Wei Lu, Lidong Bing

Syntactic Text Processing

Challenge:	Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant.
Approach:	They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact.
Outcome:	The proposed model achieves better performance than previous approaches while requiring fewer parameters.

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)

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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang

Challenge:	Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning.
Approach:	They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts.
Outcome:	The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube.

ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)

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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)

Challenge:	Recent studies have identified significant redundancy in large language models . quantization and pruning are two methods that reduce computational resources .
Approach:	They propose simple pruning methods that prune redundant layers based on their BI scores.
Outcome:	The proposed pruning methods demonstrate superior performance over previous pruning methods.

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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated.
Approach:	They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities.
Outcome:	The proposed benchmark features 4,761 diverse image sequences with varying lengths.

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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen

Challenge:	Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data.
Approach:	They propose a location-based approach that leverages locational data to optimize interaction preferences.
Outcome:	The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations.

Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation (2025.naacl-long)

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Ruichu Cai, Junhao Lu, Zhongjie Chen, Boyan Xu, Zhifeng Hao

Low-Resource NLP

Challenge:	Zero-shot Named Entity Recognition (ZS-NER) aims to recognize entities in unseen domains without specific annotated data.
Approach:	They propose a novel two-stage framework leveraging large language model techniques to improve the ZS-NER’s recall rate.
Outcome:	The proposed framework improves the ZS-NER’s recall rate and accuracy by incorporating a large language model.

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu

X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)

Challenge:	Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text.
Approach:	They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes.
Outcome:	The proposed benchmark improves by 4.1% over baselines on SCITAT.

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Xiaoya Lu, Dongrui Liu, Yi Yu, Luxin Xu, Jing Shao

ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)

Challenge:	Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline .
Approach:	They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary.
Outcome:	The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability.

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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou

Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)

Challenge:	Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts.
Approach:	They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts.
Outcome:	The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average.

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Zhicong Lu, Li Jin, Guangluan Xu, Linmei Hu, Nayu Liu, Xiaoyu Li, Xian Sun, Zequn Zhang, Kaiwen Wei

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

Challenge:	Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order.
Approach:	They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality .
Outcome:	The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward.

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Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song

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

Challenge:	Existing evaluation methods for large language models are labor-intensive and lack efficiency.
Approach:	They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background.
Outcome:	The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics.

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

Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)

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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Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli

Rethinking the Reversal Curse of LLMs: a Prescription from Human Knowledge Reversal (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications.
Approach:	They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task.
Outcome:	The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language.

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Zhicong Lu, Li Jin, Peiguang Li, Yu Tian, Linhao Zhang, Sirui Wang, Guangluan Xu, Changyuan Tian, Xunliang Cai

CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction (2026.acl-long)

Challenge:	Existing methods for large language models (LLMs) are limited by their aggressive sample permutation and lack a detailed understanding of the underlying reasons for the reversal curse.
Approach:	They propose a method which enhances bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse.
Outcome:	The proposed method overcomes the reversal curse by augmenting the samples with entity order-reversals and semantically preserved question-answer pairs.

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Sizhe Wang, Ziqi Xu, Claire Najjuuko, Charles Alba, Chenyang Lu

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)

Challenge:	Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates are poorly calibrated and clinically unreliable.
Approach:	They propose a framework that aligns clinical LM-based risk estimates and uncertainty with individual error likelihoods and cohort-level ambiguities.
Outcome:	The proposed framework improves accuracy on clinical risk prediction tasks without compromising discrimination.

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Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, Libo Qin

Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)

Challenge:	Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal.
Approach:	They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities .
Outcome:	The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs.

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Chenghao Sun, Zhen Huang, Yonggang Zhang, Le Lu, Houqiang Li, Xinmei Tian, Xu Shen, Jieping Ye

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)

Challenge:	Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored .
Approach:	They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries.
Outcome:	The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries .

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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

Decomposed Prompt Tuning via Low-Rank Reparameterization (2023.findings-emnlp)

Challenge:	Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well.
Approach:	They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet.
Outcome:	The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.

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Yao Xiao, Lu Xu, Jiaxi Li, Wei Lu, Xiaoli Li

Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)

Challenge:	Pre-trained language models have achieved remarkable performance on various tasks.
Approach:	They propose a decomposed prompt tuning approach that utilizes low-rank matrices to initialize the soft prompt.
Outcome:	The proposed method significantly reduces the number of trainable parameters while maintaining effectiveness.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang

Double Path Networks for Sequence to Sequence Learning (C18-1)

Challenge:	Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data.
Approach:	They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data.
Outcome:	The proposed approach preserves key contextual information from the original data while reducing privacy risks.

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Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu

TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding (2023.findings-emnlp)

Challenge:	Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular .
Approach:	They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning.
Outcome:	The proposed method significantly improves performance over state-of-the-art systems.

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Shuhuai Ren, Sishuo Chen, Shicheng Li, Xu Sun, Lu Hou

Visual Data in NLP

Challenge:	Experimental results show that TESTA reduces the number of visual tokens by 75% and thus accelerates video encoding.
Approach:	They propose a method to condense video semantics by aggregating similar frames and patches within each frame.
Outcome:	The proposed method reduces visual tokens by 75% and accelerates video encoding.

H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)

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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li

Challenge:	Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime .
Approach:	They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM .
Outcome:	Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets .

Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024.eacl-long)

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Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yong Liu

Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)

Challenge:	Existing methods to recommend items are categorized into attribute-based and generation-based methods.
Approach:	They propose to represent items in natural language and formulate a conversational recommender system that can be optimized in a single stage without relying on non-textual metadata.
Outcome:	The proposed model can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph.

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Wanrong Zhu, An Yan, Yujie Lu, Wenda Xu, Xin Wang, Miguel Eckstein, William Yang Wang

WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)

Challenge:	Existing tools for text-to-image synthesis can visualize machine imaginations for a given context.
Approach:	They propose a framework that uses machine-generated images to guide language models in open-ended text generation.
Outcome:	The proposed framework is effective on open-ended text generation tasks while showing minor degeneration.

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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu

Generating Textual Adversaries with Minimal Perturbation (2022.findings-emnlp)

Challenge:	Word sense disambiguation (WSD) is a fundamental task in natural language processing.
Approach:	They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization.
Outcome:	The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings.

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Xingyi Zhao, Lu Zhang, Depeng Xu, Shuhan Yuan

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

Challenge:	Existing word-level adversarial approaches for textual data have various limitations due to the large search space consisting of combinations of candidate words.
Approach:	They propose a novel attack strategy to find adversarial texts with high similarity to original texts without perturbation.
Outcome:	The proposed approach achieves higher success rates and lower perturbation rates in four benchmark datasets compared with state-of-the-art approaches.

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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

LegoMT2: Selective Asynchronous Sharded Data Parallel Training for Massive Neural Machine Translation (2025.findings-acl)

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Fei Yuan, Yinquan Lu, Lei Li, Jingjing Xu

Machine Translation

Challenge:	Existing methods to train a single model for massive languages have huge communication overheads and parameter interference.
Approach:	They propose an efficient training approach with an asymmetric multi-way model architecture for massive multilingual neural machine translation.
Outcome:	The proposed model is 16.2 faster than the distributed training method for M2M-100-12B while improving the translation performance by an average of 2.2 BLEU on Flores-101.

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction (2021.acl-long)

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Lu Xu, Yew Ken Chia, Lidong Bing

Sentiment Analysis

Challenge:	Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each word and opinion word.
Approach:	They propose a span-level approach which explicitly considers the interaction between whole spans of targets and opinions when predicting their sentiment relation.
Outcome:	The proposed approach improves on triplets with multi-word targets and opinions . it explicitly considers the interaction between whole spans of targets and opinion words .

Generating Responses with a Specific Emotion in Dialog (P19-1)

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Zhenqiao Song, Xiaoqing Zheng, Lu Liu, Mu Xu, Xuanjing Huang

Dialogue Systems & Conversational Agents

Challenge:	EmoDS can express emotions in both ways, but it is difficult to scale to large datasets.
Approach:	They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions .
Outcome:	The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression.

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

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Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

DeepSpecs: Expert-Level Question Answering in 5G (2026.findings-acl)

Challenge:	Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach:	They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome:	The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

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Aman Ganapathy Manvattira, Yifei Xu, Ziyue Dang, Songwu Lu

Intra-Correlation Encoding for Chinese Sentence Intention Matching (2020.coling-main)

Challenge:	3GPP standards define the technical design and implementation of 5G systems . expert-level questions require navigating thousands of pages of cross-referenced standards .
Approach:	They propose a standard-native retrieval-augmented generation system that can answer 5G questions . they use SpecDB, ChangeDB, TDocDB and a metadata-rich retrieval system to do this .
Outcome:	The proposed solution outperforms base models and state-of-the-art RAG systems in QA datasets . expert-level queries require navigating thousands of pages of cross-referenced standards .

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Xu Zhang, Yifeng Li, Wenpeng Lu, Ping Jian, Guoqiang Zhang

MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents (2026.acl-long)

Challenge:	Existing methods to improve sentence intention matching for Chinese text are limited due to the particularity of the text.
Approach:	They propose a method that combines character-granularity and word-granulularity features to perform sentence intention matching.
Outcome:	The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences.

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Xiangyu Wu, Yuwei Hu, Tianyu Cui, Yueying Tian, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yang Yang, Jianfeng Lu

Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)

Challenge:	Existing agent benchmarks fail to evaluate an agent's real-world capacity to handle CAPTCHA . Existing benchmarks ignore this practical challenge, failing to evaluate agents' ability to handle complex visual CAPTchas.
Approach:	They propose a benchmark annotated with Weighted Pass Rate and a new metric to measure agent's ability to handle CAPTCHA.
Outcome:	The proposed benchmark outperforms current state-of-the-art closed-source models on mirrorCAPTCHA and achieves 9.4% higher average weighted pass rate and 2.13% higher average Completion degree.

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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang

Knowledge Representation

Challenge:	Named entity recognition is a fundamental task in natural language processing.
Approach:	They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner.
Outcome:	The proposed method outperforms existing state-of-the-art models on nested and flat datasets.

IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation (2024.acl-long)

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Senyu Han, Lu Chen, Li-Min Lin, Zhengshan Xu, Kai Yu

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

Challenge:	Language models have demonstrated their capabilities in storyline creation and human-like character role-playing.
Approach:	They propose a director-actor coordinate agent framework that generates drama scripts . framework allows actors to role-play their characters while maintaining plot development .
Outcome:	The proposed framework generates drama scripts from a drama plot outline and human actors can play their characters.

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin

Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)

Challenge:	Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias.
Approach:	They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens .
Outcome:	The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data.

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Lu Xu, Lidong Bing, Wei Lu, Fei Huang

Sentiment Analysis

Challenge:	Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span.
Approach:	They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features.
Outcome:	The proposed model extracts aspect-specific opinion spans and evaluates sentiment polarity using extracted opinion features.

From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)

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Tianduo Wang, Lu Xu, Wei Lu, Shanbo Cheng

Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch (2025.acl-long)

Challenge:	Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge.
Approach:	They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models.
Outcome:	The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%.

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Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu, XingYu XingYu, Yuqiu Ji, Guohai Xu, Hongyu Lin, Ben He, Xianpei Han, Le Sun, Debing Zhang

Challenge:	Existing Chinese resources are small in scale and limited to specific domains, making them insufficient for LLM post-training.
Approach:	They propose a Chinese-annotated reward model and a preference dataset to address this gap . they evaluate Chinese RMs on CheemsBench and construct an RM that captures human preferences .
Outcome:	The proposed RM achieves state-of-the-art performance on CheemsBench and CheeMePreference.

Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement (2026.eacl-industry)

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Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, David Farris, Ryan Angilly, Santiago Pombo, Lu An, Anbang Xu, Abhinav Balasubramanian, Tan Yu, Jiaxiang Ren, Rama Akkiraju

Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)

Challenge:	NVInfo AI is a generative AI agent that can be deployed in production without full-scale retraining or infrastructure overhauls.
Approach:	They propose to implement a retrieval-augmented generation (RAG)-driven data flywheel in NVInfo AI, a mixture-of-experts knowledge assistant, for 30,000 employees.
Outcome:	The proposed system addresses failures in retrieval-augmented generation pipelines and enables continuous learning.

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Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun

Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection (2025.findings-acl)

Challenge:	Existing knowledge graphs that represent entities in different languages are not covered by existing systems.
Approach:	They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other.
Outcome:	The proposed method significantly outperforms existing systems on two benchmark datasets.

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Benjamin C Warner, Ziqi Xu, Simon Haroutounian, Thomas Kannampallil, Chenyan Lu

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)

Challenge:	Survey data often contains many features that lead to high-dimensional inputs for machine learning models.
Approach:	They propose to use semantic textual similarity scores to score features in surveys . they compare STS scores to a statistically-based scoring approach .
Outcome:	The proposed method can be used in clinical surveys to score features . it can be applied to a clinical dataset and to NIH All of Us dataset .

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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu

GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)

Challenge:	Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs.
Approach:	They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets.
Outcome:	Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks.

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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge.
Approach:	They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations.
Outcome:	The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

Explainability & Interpretability in NLP

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction (2026.acl-long)

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Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)

Challenge:	Emergency departments rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care.
Approach:	They propose a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction based on the SOAP paradigm .
Outcome:	The proposed framework outperforms prompting-based, multi-agent, and encoder-based baselines.

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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)

Challenge:	Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization.
Approach:	They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios.
Outcome:	The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset.

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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu

Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)

Challenge:	Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing.
Approach:	a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials.
Outcome:	The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance .

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Xiang Cheng, Yulan Hu, Xiangwen Zhang, Lu Xu, Lide Tan, Zheng Pan, Xin Li, Yong Liu

Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs (2022.naacl-main)

Challenge:	Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries.
Approach:	They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios.
Outcome:	The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities.

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Xu Wang, Simin Fan, Jessica Houghton, Lu Wang

Bridging the Gap between Different Vocabularies for LLM Ensemble (2024.naacl-long)

Challenge:	NLP-powered automatic question generation (QG) techniques have not been widely adopted in classrooms to date.
Approach:	They propose to identify key impediments and improve the usability of NLP-powered automatic question generation techniques by understanding how instructors construct questions and identifying touch points to enhance the underlying NLP models.
Outcome:	The proposed methods can be used by 11 instructors across 7 universities and highlight their needs and needs when creating questions.

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Yangyifan Xu, Jinliang Lu, Jiajun Zhang

Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents (2025.findings-emnlp)

Challenge:	Existing methods to combine large language models with complete outputs have limited effectiveness . lexical gaps between different LLMs hinder dynamic correction and enhancement .
Approach:	They propose a method to ensemble large language models via Vocabulary Alignment (EVA) they learn mappings between vocabularies of different LLMs with overlapping tokens .
Outcome:	The proposed method bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step.

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Rui Xu, Mingyu Wang, Xintao Wang, Dakuan Lu, Xiaoyu Tan, Wei Chu, Xu Yinghui

i-Code Studio: A Configurable and Composable Framework for Integrative AI (2024.emnlp-demo)

Challenge:	Recent advances in Large Language Models (LLMs)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications.
Approach:	They propose a benchmark for evaluating character thought generation using literature . they propose 'MIRROR' which generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations.
Outcome:	The proposed benchmark outperforms existing methods in evaluating character thought generation.

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Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	Existing frameworks for Integrative AI lack flexibility and composability to handle multimodal tasks.
Approach:	They propose a configurable framework for Integrative AI that orchestrates multiple pre-trained models to conduct complex multimodal tasks.
Outcome:	The proposed framework achieves impressive results on zero-shot multimodal tasks . it can communicate and personalize for users, and it can be used in a multimodal agent .

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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)

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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting.
Approach:	They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status.
Outcome:	The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B).

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Enhancing One-Shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism (2025.coling-main)

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

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Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Yixing Xu, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum

Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)

Challenge:	Pre-trained language models (PLMs) are robust in contextual understanding but their considerable size incurs significant computational and storage costs.
Approach:	They propose a Sparse-Dense-Sparse pruning framework to prune PLMs . they prune less critical connections using conventional pruning methods .
Outcome:	The proposed pruning framework outperforms SparseGPT and Wanda under identical sparsity.

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Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi

PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning (2025.acl-long)

Challenge:	Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them.
Approach:	They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule.
Outcome:	The proposed method outperforms existing methods in two 5-task MTL setups.

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Zhicong Lu, Changyuan Tian, PeiguangLi PeiguangLi, Li Jin, Sirui Wang, Wei Jia, Ying Shen, Guangluan Xu

CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution (2025.acl-long)

Challenge:	Existing studies on Large Language Models (LLMs) have failed to evaluate their performance in event reasoning with a single event relational type or reasoning format.
Approach:	They propose a benchmark to evaluate LLMs' event reasoning capability using a single event relational type or reasoning format.
Outcome:	The proposed model improves on 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity.

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Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun

Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation (2026.acl-demo)

Challenge:	Existing code benchmarks focus on code generation, while those for code reasoning are insufficient.
Approach:	They propose a multi-lingual code reasoning benchmark that contains 19 programming languages and at least 600 subjects for each language.
Outcome:	The proposed model trains on Python and achieves 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.

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Mingyu Wang, Bin Yu, Wenjian Lu, Zhi Wang, Gao Kefeng, Cheng Wen, Xu Lu, Cong Tian

Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions (2024.emnlp-main)

Challenge:	Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices.
Approach:	They present a tool that generates QEMU-based virtual devices directly from Linux driver source code.
Outcome:	The proposed tool generates QEMU-based virtual devices directly from Linux driver source code.

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Inderjeet Nair, Jiaye Tan, Xiaotian Su, Anne Gere, Xu Wang, Lu Wang

RAISE: Reinforced Adaptive Instruction Selection For Large Language Models (2025.findings-emnlp)

Challenge:	Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes.
Approach:	They propose a tool that PROduces Feedback via learning from LM simulated student revisions and propose to iteratively optimize the feedback generator by directly maximizing the effectiveness of students’ overall revising performance.
Outcome:	The proposed approach surpasses baseline methods in effectiveness of improving students’ writing and demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect.

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Qingsong Lv, Yangning Li, Zihua Lan, Zishan Xu, Jiwei Tang, Tingwei Lu, Yinghui Li, Wenhao Jiang, Hong-Gee Kim, Hai-Tao Zheng, Philip S. Yu

Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)

Challenge:	Existing selection methods rely on static, heuristic quality scores and are executed only once before training.
Approach:	They propose a dynamic selection framework that integrates selection into every training step.
Outcome:	The proposed framework integrates selection into every training step.

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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia

Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.acl-long)

Challenge:	Existing benchmarks focus on evaluating MLLMs’ pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning.
Approach:	They propose a benchmark designed for visual clue-driven reasoning in daily scenarios that combines rigorous grounding in authentic daily activities and challenging query design that necessitates more than surface-level perception.
Outcome:	The proposed benchmark identifies visual clues and their ability to provide robust reasoning in daily scenarios.

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Minzheng Wang, Run Luo, Yanbo Wang, Zichen Liu, Yuqiao Tan, Tao Tan, Nan Xu, Lu Wang, Wenji Mao