Papers by Yuan Sun

PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes (2025.emnlp-main)

Challenge:	Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities .
Approach:	They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens.
Outcome:	The proposed model improves the performance of LLMs for IE with zero-shot generalization.

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Zhijun Xu, Siyu Yuan, Yiqiao Zhang, Jingyu Sun, Tong Zheng, Deqing Yang

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)

Challenge:	Pun memes combine wordplay with visual elements to create humor, irony, or other rhetorical effects.
Approach:	They propose a benchmark to assess Chinese pun memes' processing capabilities across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response.
Outcome:	The proposed model can detect pun memes, analyze sentiments, and respond to chats, while ignoring homophone wordplay.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)

Challenge:	Existing models of robustness evaluation are incomprehensive, impractical, and invalid .
Approach:	They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks.
Outcome:	The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol.

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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity (2026.findings-acl)

Challenge:	Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data.
Approach:	They propose a framework that aligns music modalities with multilingual text in a shared representation space.
Outcome:	Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts .

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Zheyuan Yang, Liqiang Shang, Junjie Chen, Xun Yang, Chenglong Xu, Bo Yuan, Chenyuan Jiao, Yaoru Sun, Yilun Zhao

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

Challenge:	TableVista evaluates multimodal table reasoning under visual and structural complexity . current models struggle to maintain reasoning consistency when structural complexity combined with visually integrated presentations.
Approach:	They propose a benchmark for evaluating multimodal table reasoning under visual and structural complexity.
Outcome:	The proposed model performs poorly on visual and structural complexity.

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

MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)

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

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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han

Information Retrieval

Challenge:	Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead.
Approach:	They propose an agent framework that maintains a compact memory during multi-turn interactions.
Outcome:	The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions.

Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)

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Xiaoya Li, Yuxian Meng, Xiaofei Sun, Qinghong Han, Arianna Yuan, Jiwei Li

Representation Learning

Challenge:	Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words .
Approach:	They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks.
Outcome:	The proposed model outperforms char-based models in four NLP benchmark tasks.

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Reasoning

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)

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Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun

A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP).
Approach:	They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages.
Outcome:	The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration.

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Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun, Caixia Yuan, Xiaojie Wang

Dialogue Systems & Conversational Agents

Challenge:	Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns.
Approach:	They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD.
Outcome:	The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions .

Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)

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Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu

Information Retrieval

Challenge:	Existing approaches for personalizing large language models require modifying parameters.
Approach:	They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue .
Outcome:	The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks.

Enhancing Agent Learning through World Dynamics Modeling (2024.findings-emnlp)

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Zhiyuan Sun, Haochen Shi, Marc-Alexandre Côté, Glen Berseth, Xingdi Yuan, Bang Liu

Challenge:	Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics.
Approach:	They propose a framework that discovers world dynamics from a small number of demonstrations, verifies the correctness of these dynamics, and evolves new, advanced dynamics tailored to the current situation.
Outcome:	The proposed framework discovers, verifies, and evolves world dynamics from a small number of demonstrations, and compares the automatically generated dynamics with human-annotated world dynamics.

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Challenge:	Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach:	They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome:	The proposed framework can be used to generate presentations with environmental observations.

CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages (2025.coling-main)

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Wenhao Zhuang, Yuan Sun

Multilinguality

Challenge:	Existing multilingual models such as XLM-R support only approximately 100-200 languages, leaving nearly 7,000 low-resource languages untapped.
Approach:	They construct and open-source a dataset of four-language corpora obtained through machine translation into Chinese, Uyghur and Tibetan.
Outcome:	The proposed dataset includes two resource-rich languages and two low-resource languages.

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

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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.

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

Personalized Large Language Model Assistant with Evolving Conditional Memory (2025.coling-main)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Ruifeng Yuan, Shichao Sun, Yongqi Li, Zili Wang, Ziqiang Cao, Wenjie Li

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

Challenge:	With the rapid development of large language models, personalized large language model assistants like ChatGPT are limited in personalized services.
Approach:	They propose a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory.
Outcome:	The proposed framework can preserve the knowledge and experience from the history dialogue with the user, which can be applied to future tailored responses that better align with the users' preferences.

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Weize Chen, Jiarui Yuan, Chen Qian, Cheng Yang, Zhiyuan Liu, Maosong Sun

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution.
Approach:	They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training.
Outcome:	The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange.

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)

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Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan

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

Challenge:	Named entity recognition (NER) is a fundamental task of information extraction.
Approach:	They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity.
Outcome:	The proposed model performs better on standard NER benchmarks than other models on open datasets.

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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

ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)

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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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han

Dialogue Systems & Conversational Agents

Challenge:	Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates.
Approach:	They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent.
Outcome:	The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets.

Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)

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Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Challenge:	Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences .
Approach:	They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives .
Outcome:	The proposed models can provide responses that match various preferences among the ”3H” desiderata.

Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution (2021.acl-long)

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Fanchao Qi, Yuan Yao, Sophia Xu, Zhiyuan Liu, Maosong Sun

Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration (2025.acl-long)

Challenge:	Recent studies show that neural natural language processing models are vulnerable to backdoor attacks.
Approach:	They propose to inject neural models with backdoors activated by word substitution . their results raise a serious alarm to the security of NLP models, they argue .
Outcome:	The proposed backdoors are activated by a learnable combination of word substitution and exhibit higher invisibility than previous methods.

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Tianyi Bai, Ling Yang, Zhen Hao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Qiu Jiantao, Wentao Zhang, Binhang Yuan, Conghui He

Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)

Challenge:	Efficient data selection is crucial to accelerate the pretraining of language models . limited research has addressed the inherent conflicts between data selection methods .
Approach:	They propose a multi-actor collaborative data selection mechanism that prioritizes data based on its specific criterion and updates prioritization rules using the current state of the model.
Outcome:	The proposed model accelerates convergence in LM pretraining and achieves an average relative performance gain of 10.5% across multiple language model benchmarks.

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Ruifeng Yuan, Shichao Sun, Zili Wang, Ziqiang Cao, Wenjie Li

Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)

Challenge:	Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance.
Approach:	They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting .
Outcome:	The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings.

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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang

Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)

Challenge:	Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks.
Approach:	They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem.
Outcome:	The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings.

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Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun

PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)

Challenge:	Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms.
Approach:	They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately.
Outcome:	The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods.

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Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)

Challenge:	Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks.
Approach:	They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework.
Outcome:	The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks.

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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun

SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)

Challenge:	Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined.
Approach:	They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process.
Outcome:	The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness.

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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni

VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models (2026.acl-long)

Challenge:	Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging .
Approach:	They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks.
Outcome:	The proposed framework bridges the domain gap between LLMs and recommendation tasks.

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Huawei Ji, Yuanhao Sun, Yuan Jin, Cheng Deng, Jiaxin Ding, Luoyi Fu, Xinbing Wang

Visual Data in NLP

Challenge:	Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance.
Approach:	They propose a framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations.
Outcome:	The proposed framework approximates the empirical Pareto frontier obtained through grid search and generalizes well across pruning methods and VLM architectures.

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)

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Xu Han, Tianyu Gao, Yuan Yao, Deming Ye, Zhiyuan Liu, Maosong Sun

Entrospect: Information-Theoretic Self-Reflection Elicits Better Response Refinement of Small Language Models (2025.findings-acl)

Challenge:	OpenNRE provides a framework to implement neural relation extraction (RE) . the toolkit provides various functional modules based on TensorFlow and PyTorch .
Approach:	OpenNRE is an open-source framework to implement neural relation extraction models. they also release an online system to meet real-time extraction without any training and deployment.
Outcome:	OpenNRE provides a framework to implement neural models for relation extraction (RE) the toolkit also includes an online system to meet real-time extraction without training and deployment .

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Tianqiang Yan, Ziqiao Lin, Lin Zhang, Zhenglong Sun, Yuan Gao

ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)

Challenge:	Existing approaches to self-reflection fail to deliver robust response refinement for models with parameter sizes of 10 billion or smaller.
Approach:	They propose to redesign Self-Refine and introduce an information-theoretic framework based on Chain-of-Thought prompt engineering to improve self-reflection in Small Language Models.
Outcome:	The proposed framework improves reasoning accuracy and computational efficiency by up to 36.2% under identical model and data settings.

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Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, Maosong Sun

KS-Lottery: Finding Certified Lottery Tickets for Multilingual Transfer in Large Language Models (2025.naacl-long)

Challenge:	Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness.
Approach:	They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks.
Outcome:	The proposed method can handle all the textual backdoor attack situations.

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Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun, Lei Li

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)

Challenge:	Existing studies have shown that a small subset of parameters is highly effective in fine-tuning . prior work shows that there are a few additional parameters corresponding to an intrinsic dimension in a well-trained Large Language Model.
Approach:	They propose a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning.
Outcome:	The proposed method can find the certified winning tickets in the embedding layer, and fine-tuning on the found parameters is guaranteed to perform as well as full fine- tuning.

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Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, Maosong Sun

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans.
Approach:	They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.
Outcome:	The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

Graph-Guided Textual Explanation Generation Framework (2025.emnlp-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein

Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)

Challenge:	Existing work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer.
Approach:	They propose a Graph-Guided Textual Explanation Generation framework that generates a graph neural network layer that guides the NLE generation and generates explanations with greater semantic and lexical similarity to human-written ones.
Outcome:	The proposed framework improves NLE faithfulness by up to 12.12% compared to baseline methods on encoder-decoder and decoder-only models.

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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang

Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization (2024.findings-acl)

Challenge:	Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks.
Approach:	They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter.
Outcome:	The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA.

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Shichao Sun, Ruifeng Yuan, Ziqiang Cao, Wenjie Li, Pengfei Liu

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

Challenge:	Large language models (LLMs) can improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft.
Approach:	They propose to use Prompt Chaining and Stepwise Prompting to perform iterative refinement . they propose to combine the two methods to produce a more favorable outcome .
Outcome:	The proposed methods can improve summary quality by mirroring a human-like iterative process . the results show that the prompt chaining method produces a more favorable outcome .

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents (2025.emnlp-main)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang

Open Hierarchical Relation Extraction (2021.naacl-main)

Challenge:	Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.
Approach:	They propose a new LLM-based Multi-Agent System benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
Outcome:	The proposed benchmark provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication.

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Kai Zhang, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation (2022.findings-acl)

Challenge:	Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency.
Approach:	They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations.
Outcome:	The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion.

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Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu

Challenge:	Existing methods to measure instance difficulty use generalization and threshold-tuning . a new approach to learn to exit is based on hash functions to assign tokens to a fixed exiting layer.
Approach:	They propose a Hash-based Early Exiting approach that replaces learn-to-exit modules with hash functions to assign each token to a fixed exiting layer.
Outcome:	The proposed approach improves on learning to exit and predicting instance difficulty.

OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)

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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun

Scaling Laws or Threshold Effects: Exploring the Optimal Vocabulary Size for Balancing Performance and Efficiency in Low-Resource Languages (2026.findings-acl)

Challenge:	Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models.
Approach:	They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit.
Outcome:	The proposed toolkit supports all attack types, multilinguality, and parallel processing.

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Ao Han, Andong Chen, Yuan Sun, Xiaobing Zhao

Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)

Challenge:	vocab expansion scaling laws are well-established for high-resource languages, but they remain unverified in low-resourced settings.
Approach:	They propose to scale trilingual vocabulary for languages with 140 to 195,000 tokens . they find that BBPE follows a "decline-then-rise" pattern, whereas BPE improves monotonically .
Outcome:	The proposed configuration reduces pre-training duration by over 71% across 1.5B to 8B models while improving downstream performance.

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Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

Challenge:	Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive.
Approach:	They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data.
Outcome:	Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods.

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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun

Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)

Challenge:	Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach:	They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome:	The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu

The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)

Challenge:	Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks.
Approach:	They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability.
Outcome:	The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios.

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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun

CoTD-PO: Chain-of-Thought Distillation with Preference Optimization (2025.findings-emnlp)

Challenge:	Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations.
Approach:	They propose a training data arrangement framework that allows for continual learning and loss reduction.
Outcome:	The proposed framework promotes continual learning and loss reduction on unseen tasks.

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Lujie Niu, Haochen Sun, Fangkun Zhao, Sheng Chen, Zimeng Bai, Jiawei Zhang, Caixia Yuan, Xiaojie Wang

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

Challenge:	Existing methods for chain-of-thought distillation suffer from a distribution mismatch between teacher-generated training trajectories and the student model's own generative distribution.
Approach:	They propose a framework that shifts the training paradigm from passive imitation to active trajectory exploration by allowing students to sample their own answer paths.
Outcome:	The proposed method outperforms standard CoT distillation baselines while mitigating mode collapse and preserving semantic diversity.

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

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

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

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

Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021.acl-long)

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

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Lianwei Wu, Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi

GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)

Challenge:	Existing studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification.
Approach:	They propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) that captures word-level semantics based . on individual cognition, they adjust the proportion between them to generate global evidence.
Outcome:	The proposed model is based on the views of collective and individual cognition and achieves state-of-the-art performance on three benchmark datasets.

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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun

Visual Data in NLP

Challenge:	Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction.
Approach:	They propose a series of datasets for training visual-based GUI agents using general VLMs.
Outcome:	The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks.

ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base (2024.acl-long)

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Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models (2025.acl-long)

Challenge:	ANALOGYKB is a million-scale analogy knowledge base based on existing knowledge graphs (KGs) based upon relational knowledge triples, we can discover new analogies using the corresponding relations between concepts.
Approach:	They propose a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) ANALOGYKB identifies analogies of the same relations and analogies from analogous relations .
Outcome:	The proposed model enables both smaller LMs and LLMs to gain better analogical reasoning capabilities.

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Zenghui Yuan, Yangming Xu, Jiawen Shi, Pan Zhou, Lichao Sun

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

Challenge:	Existing research on model merging focuses on optimizing model performance and minimizing backdoors.
Approach:	They propose a backdoor attack targeting model merging in Large Language Models that creates a unified model for multi-domain tasks.
Outcome:	The proposed attack is effective across models, merging algorithms, and tasks while maintaining utility across tasks.

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu

Enhancing Cross-Lingual Transfer through Reversible Transliteration: A Huffman-Based Approach for Low-Resource Languages (2025.acl-long)

Challenge:	Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) .
Approach:	They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs).
Outcome:	The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement.

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Wenhao Zhuang, Yuan Sun, Xiaobing Zhao

Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)

Challenge:	Large language models demonstrate cross-lingual transfer capabilities, but these capabilities often fail to extend to low-resource languages, especially those utilizing non-Latin scripts.
Approach:	They propose to combine character transliteration with Huffman coding to create a complete transliterations framework that can be extended to other low-resource languages.
Outcome:	The proposed framework reduces storage requirements and improves accuracy and accuracy across multiple downstream tasks while maintaining performance on high-resource languages.

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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu

LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are capable of performing tasks but are likely to be misused.
Approach:	They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model .
Outcome:	The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts .

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Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu

How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)

Challenge:	distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction.
Approach:	They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections.
Outcome:	The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets.

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Tianjie Ju, Weiwei Sun, Wei Du, Xinwei Yuan, Zhaochun Ren, Gongshen Liu

MonCulture-Eval: A Hierarchical Benchmark for Evaluating Mongolian Cultural Capabilities of Large Language Models across Scripts and Regions (2026.findings-acl)

Challenge:	Existing studies have focused on enhancing the factualness of large language models using context knowledge.
Approach:	They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts.
Outcome:	The proposed model can encode knowledge across different layers, and it is compared with existing models.

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Quulgan Minggad, Xiao Zinan, Yuan Sun

MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)

Challenge:	Large Language Models excel at multilingual translation and instruction-following in low-resource settings like Tibetan, but lack cultural intelligence quantification.
Approach:	They propose a benchmark to assess the cultural intelligence of Large Language Models in Mongolia . they use a three-layer cognitive hierarchy and specialized tasks to assess their cultural intelligence .
Outcome:	The monCulture-Eval benchmark assesses the cultural intelligence of large language models in the Mongolian context across two writing systems and three regional sub-cultures.

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Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth

Multilinguality

Challenge:	Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns.
Approach:	They propose a framework that condenses chat history in the background without disrupting the user experience.
Outcome:	The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns.

Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)

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Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin

Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)

Challenge:	Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents.
Approach:	They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks.
Outcome:	The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark.

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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Programming Languages in NLP

Challenge:	Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability.
Approach:	They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths.
Outcome:	The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness.

Fine-Grained Legal Argument-Pair Extraction via Coarse-Grained Pre-training (2024.lrec-main)

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Chaojun Xiao, Yutao Sun, Yuan Yao, Xu Han, Wenbin Zhang, Zhiyuan Liu, Maosong Sun

RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)

Challenge:	Current methods conceptualize LAE as a supervised sentence-pair classification problem and necessitate extensive manual annotations.
Approach:	They propose a model that focuses on fine-grained alignment of argument pairs building upon coarse-grain complaint-defense pairs.
Outcome:	The proposed model outperforms baseline models by 3.7 and 2.4 points on average.

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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages (2026.acl-long)

Challenge:	Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.
Approach:	They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph.
Outcome:	The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts.

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Yijie Li, Xi Cao, Yuan Sun, Quulgan Minggad, Abdulla Ablikim, Jia Qing Cai Wang

Machine Translation

Challenge:	CMiLBench is a framework to evaluate linguistically and culturally diverse minority languages . rapid evolution of LLMs has revolutionized NLP, but progress is unevenly distributed .
Approach:	They propose a framework to translate a theoretical notion of "diversity in unity" into practical evaluation for three minority languages . CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks .
Outcome:	The proposed framework evaluates 14 state-of-the-art LLMs with a hybrid framework . it integrates automatic metrics and LLM-as-a-Judge scoring .

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

The Critique of Critique (2024.findings-acl)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Shichao Sun, Junlong Li, Weizhe Yuan, Ruifeng Yuan, Wenjie Li, Pengfei Liu

Challenge:	MetaCritique builds specific quantification criteria to evaluate the quality of critique . a systematic method to evaluate critique is lacking.
Approach:	They propose a critique of critique, termed MetaCritique, which builds specific quantification criteria and aggregates each AIU's judgment for the overall score.
Outcome:	The proposed method can achieve near-human performance across 16 datasets.

A Computational Simulation of Language Production in First Language Acquisition (2025.emnlp-main)

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Yuan Gao, Weiwei Sun

Text Error Correction

Challenge:	Existing computational studies of child language acquisition focus on isolated mechanisms, such as spreading activation in retrieval, sentence planning, or production efficiency.
Approach:	They propose a computational framework for modeling child language production using graphs to formalize meaning and Synchronous Hyperedge Replacement Grammar to formalized the syntax–semantics interface.
Outcome:	The proposed framework is based on graphs to formalize meaning and Synchronous Hyperedge Replacement Grammar (SHRG) resulting interpretable grammars are evaluated by their ability to generate utterances .

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG (2026.acl-long)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Chuang Zhou, Zheng Yuan, Linhao Luo, Zhaozhuo Xu, Yilin Xiao, Junnan Dong, Siyu An, di Yin, Xing Sun, Xiao Huang

Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)

Challenge:	Retrieval-augmented generation (RAG) has been used for enhancing large language models with external knowledge.
Approach:	They propose a framework for mining efficient graph structures via hashing to enhance RAG . they adopt an inductive paradigm where global graph structure emerges from local hash collisions .
Outcome:	The proposed framework outperforms existing baselines while requiring no GPU resources or token budget.

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Bairu Hou, Fanchao Qi, Yuan Zang, Xurui Zhang, Zhiyuan Liu, Maosong Sun

Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)

Challenge:	Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness.
Approach:	They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory.
Outcome:	The proposed method achieves significantly better performance than baseline methods.

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Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li

Natural Language Interfaces

Challenge:	Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts.
Approach:	They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem .
Outcome:	The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets.

Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)

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Yuan Sun, Sisi Liu, Zhengcuo Dan, Xiaobing Zhao

Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026.acl-long)

Challenge:	Recent research on question generation has achieved great success, but some question types and answers did not match.
Approach:	They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions.
Outcome:	The proposed model improves the accuracy of interrogative words in generated questions.

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Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

Challenge:	Large Language Models exhibit strong implicit personalization ability, but most approaches treat this behavior as a black box.
Approach:	They propose a mechanistic interpretation perspective and propose 'sparse' set of Preference Heads . they compute a Preference Contribution Score for each attention head and compare their predictions .
Outcome:	The proposed framework computes a Preference Contribution Score (PCS) for each attention head and measures its causal impact on user aligned outputs.

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth (2026.eacl-long)

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

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Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, Jyotika Singh, Sandip Ghoshal, Yao-Ting Wang, Miguel Ballesteros, Yassine Benajiba

EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs (2025.coling-demos)

Challenge:	despite advances in transformers, their theoretical limitations in discrete reasoning remain a critical open problem.
Approach:	They synthesize recent advances from three theoretical perspectives to clarify structural and computational barriers transformers face when performing symbolic computations.
Outcome:	The proposed models excel at pattern matching and interpolation, but they face bottlenecks in communication and depth constraints.

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Yijie Li, Yuan Sun

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)

Challenge:	Existing open-source evaluation models lack a user-friendly visualization tool and are not optimized for accelerated model inference.
Approach:	They propose to use open-source evaluation models to evaluate language model responses.
Outcome:	The proposed model is lightweight, precise, efficient, and user-friendly, with an intuitive visualization interface for ease of deployment and use.

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Qiushi Sun, Jingyang Gong, Lei Li, Qipeng Guo, Fei Yuan

Challenge:	Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive.
Approach:	They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory.
Outcome:	The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels.

OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following (2024.acl-long)

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Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre Côté, Bang Liu

Visual Data in NLP

Challenge:	Embodied Instruction Following (EIF) is a crucial task in embodied learning . however, there is n'a unified understanding regarding the impact of various components on task performance .
Approach:	They propose a framework that delineates the core components essential for embodied learning tasks . they integrate a multi-agent design into the Planner component of their LLM-centric architecture .
Outcome:	OPEx delineates the core components essential for solving embodied learning tasks . integrating a multi-agent design into the Planner component of the LLM-centric architecture further elevates performance.

Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)

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Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Existing methods for detecting fake news use shared features as complementarity features without selection.
Approach:	They propose a sifted multi-task learning method with a selected sharing layer for fake news detection.
Outcome:	The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets.

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun