Papers by Zheng Sun

CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Model Composition for Multimodal Large Language Models (2024.acl-long)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

Multilinguality

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

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

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

LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)

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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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang

RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)

Challenge:	Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens.
Approach:	They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains.
Outcome:	The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy.

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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma

Challenge:	Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation.
Approach:	They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation.
Outcome:	The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics.

OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)

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Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, Maosong Sun

Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (2024.lrec-main)

Challenge:	Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation.
Approach:	They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework.
Outcome:	The proposed framework is easy to use and flexible enough to integrate with other frameworks.

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Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

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

Challenge:	Recent advances in large language models (LLMs) have revolutionized the field of natural language processing and artificial intelligence, creating new SOTAs and reaching human-level language understanding performance on a series of tasks and benchmarks.
Approach:	They propose to use an algorithm test set sourced from Introduction to Algorithm to assess LLMs' code execution abilities.
Outcome:	The proposed model can execute programs described in natural language as long as no heavy numeric computation is involved.

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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.

Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)

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Jun Sun, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang, Shu-Kai Zheng, Yulong Liu, Yuxin Huang, Taihao Li

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

Challenge:	Existing studies focus on developing models that exploit the unification of multiple modalities.
Approach:	They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities.
Outcome:	The proposed model outperforms state-of-the-art models in multi-modal emotion recognition.

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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.

AdaV: Adaptive Text-visual Redirection for Vision-Language Models (2025.findings-acl)

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Jiayi Han, Liang Du, Yiwen Wu, Guanming Liang, Xiangguo Zhou, Weibo Zheng, Donghong Han, Zixun Sun

Visual Data in NLP

Challenge:	Vision-language models often generate excessive visual tokens, leading to poor performance . a novel training-free visual token pruning method is proposed to improve performance despite the computational cost associated with VLMs.
Approach:	They propose a training-free visual token pruning method that reduces biased token pruning . they plan to open-source the code upon publication .
Outcome:	The proposed method reduces biased token pruning and enhances model robustness with limited visual token budget.

Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection (2022.findings-emnlp)

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Kai Zheng, Qingfeng Sun, Yaming Yang, Fei Xu

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)

Challenge:	Stance Detection Tasks require background knowledge especially when there is no explicit target mentioned in text.
Approach:	They propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from pre-trained models.
Outcome:	The proposed model is effective in stance detection on three benchmarks.

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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph .
Approach:	They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm.
Outcome:	The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

Linguistics & Cognitive NLP

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Question Answering

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)

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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun

Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)

Challenge:	Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus.
Approach:	They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture.
Outcome:	The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.

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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun

Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)

Challenge:	Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities.
Approach:	They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation.
Outcome:	The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement.

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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang

Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)

Challenge:	Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available.
Approach:	They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation.
Outcome:	The proposed method outperforms baselines on both text classification and generation tasks.

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Jiquan Wang, Lin Sun, Yi Liu, Meizhi Shao, Zengwei Zheng

Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)

Challenge:	Existing methods to detect sarcasm target with text lacking context are not sufficient and complete.
Approach:	They propose a multi-modal sarcasm target identification task that performs both textual and visual detection.
Outcome:	The proposed model can perform textual target labeling and visual target detection.

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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang

Low-Resource NLP

Challenge:	Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored.
Approach:	They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Outcome:	The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models.

Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)

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Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Jian Sun, Nevin L. Zhang

Locally Differentially Private In-Context Learning (2024.lrec-main)

Challenge:	Existing generative replay methods use only a single task-specific token to control their models.
Approach:	They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation.
Outcome:	The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems.

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Chunyan Zheng, Keke Sun, Wenhao Zhao, Haibo Zhou, Lixing Jiang, Shaoyang Song, Chunlai Zhou

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Large pretrained language models (LLMs) have shown surprising In-Context Learning ability.
Approach:	They propose a locally differentially private framework of in-context learning for LLMs that can be augmented with a private database for some specific task.
Outcome:	The proposed framework can predict labels without additional parameter modifications without input-label pairs .

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Tell Me What You Don’t Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing (2025.findings-acl)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Changze Lv, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang

Representation Learning

Challenge:	Role-playing Agents (RPAs) struggle to recognize and respond to hard queries that conflict with their role-play knowledge.
Approach:	They propose a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model’s refusal accuracy.
Outcome:	The proposed model improves RPAs’ refusal ability of conflicting requests while maintaining their general role-playing capabilities.

Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation (2024.emnlp-main)

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Ruotong Pan, Boxi Cao, Hongyu Lin, Xianpei Han, Jia Zheng, Sirui Wang, Xunliang Cai, Le Sun

Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)

Challenge:	Existing RAG paradigms suffer from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated output.
Approach:	They propose a framework that empowers models to discern and process information based on its credibility.
Outcome:	The proposed framework outperforms existing models with retrieval augmentation and exhibits robustness despite increasing noise in the context.

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Jinwen Chen, Hainan Zhang, Fei Sun, Qinnan Zhang, Sijia Wen, Ziwei Wang, Zhiming Zheng

Robustness in NLP

Challenge:	Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers.
Approach:	They propose to efficiently remove poisoned examples before or during fine-tuning .
Outcome:	The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset.

Adversarial Knowledge Stimulated Contrastive Prompting for Few-shot Language Learners (2023.findings-acl)

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Kai Zheng, Qingfeng Sun, Yaming Yang, Tengchao Lv, Yeyong Pi, Changlin Zhao, Fei Xu, Qi Zhang

On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained language models on few-shot Natural Language Understanding (NLU) tasks by employing task-specific prompts.
Approach:	They propose a Cloze-driven prompt framework for prompt tuning that implicitly stimulates knowledge from pre-trained language models.
Outcome:	The proposed framework outperforms state-of-the-art for prompt-based fine-tuning on few-shot NLU tasks.

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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang

FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)

Challenge:	Dialogue safety problems severely limit the real-world deployment of generative conversational models.
Approach:	They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings.
Outcome:	The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples.

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Jun Yin, Pengyu Zeng, Haoyuan Sun, Yuqin Dai, Han Zheng, Miao Zhang, Yachao Zhang, Shuai Lu

Challenge:	Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge.
Approach:	They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences.
Outcome:	The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning (2025.findings-acl)

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Yinlong Xu, Yanzhao Zheng, Shuoshuo Sun, Shuaihan Huang, Baohua Dong, Zhu Hangcheng, Ruohui Huang, Gang Yu, Hongxia Xu, Jian Wu

Multimodal Dialogue Response Generation (2022.acl-long)

Challenge:	Existing reasoning paradigms that focus on local optimum reasoning lack global perspective.
Approach:	They propose a bidirectional reasoning paradigm that generates reasoning paths by bidirectional planning and bottom-up reasoning accumulation.
Outcome:	The proposed reasoning paradigm outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks.

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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

Punctuation-Steered Representation Fine-Tuning (2026.acl-short)

Challenge:	Existing studies focus on multimodal dialogue models but neglect generation methods.
Approach:	They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain.
Outcome:	Experiments show that the proposed model can generate informative text and high-resolution image responses.

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Zheng Gong, Ying Sun, Ping Li, Yi Zheng, Zhefeng Wang

Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (2023.emnlp-main)

Challenge:	Existing methods for parameter-efficient fine-tuning (PeFT) are limited due to their prohibitive size and computational demands.
Approach:	They propose a method that fine-tunes punctuation representations to achieve performance improvements.
Outcome:	The proposed method improves performance by altering the representation space alone . but it results in suboptimal performance due to the effects of the method on the output .

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Junhan Yang, Zheng Liu, Chaozhuo Li, Guangzhong Sun, Xing Xie

BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)

Challenge:	Existing PLMs are infeasible for processing long documents due to computational costs and incomprehensive document understanding.
Approach:	They propose a retrieval model that models local semantics and global context semantics in a tightly-coupled manner.
Outcome:	The proposed model overcomes three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.

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Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates

Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)

Challenge:	Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion.
Approach:	They propose a query expansion model that leverages the BERT model to select relevant document chunks for expansion.
Outcome:	The proposed model significantly outperforms existing models on the TREC Robust04 and GOV2 test collections.

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Xu Han, Yuqi Luo, Weize Chen, Zhiyuan Liu, Maosong Sun, Zhou Botong, Hao Fei, Suncong Zheng

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

Challenge:	Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages.
Approach:	They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
Outcome:	The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data.

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction (2024.emnlp-main)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Zheye Deng, Chunkit Chan, Weiqi Wang, Yuxi Sun, Wei Fan, Tianshi Zheng, Yauwai Yim, Yangqiu Song

Fast Nearest Neighbor Machine Translation (2022.findings-acl)

Challenge:	Existing approaches to condensing textual information into concise and structured tables are limited in their applicability in broader contexts.
Approach:	They propose a benchmark dataset for generating summary tables of competitions based on real-time commentary texts that incorporates large-scale textual information into concise and structured tables.
Outcome:	The proposed method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets.

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Yuxian Meng, Xiaoya Li, Xiayu Zheng, Fei Wu, Xiaofei Sun, Tianwei Zhang, Jiwei Li

Machine Translation

Challenge:	Fast kNN-MT uses the entire corpus as the datastore for the nearest neighbor search . knn-MT is two-orders slower than vanilla MT models .
Approach:	They propose a fast kNN-MT model that uses the entire corpus as the datastore for nearest neighbor search.
Outcome:	The proposed model is two-orders faster than kNN-MT and is only two times slower than the standard model.

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents (2026.eacl-industry)

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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Minjia Wang, Yunfeng Wang, Xiao Ma, Dexin Lv, Qifan Guo, Lynn Zheng, Benliang Wang, Lei Wang, Jiannan Li, Yongwei Xing, Junzhe Xu, Zheng Sun

Challenge:	Publicly available corpora cover only slivers of human activity, such as email threads, chat logs, purchase histories, sensor traces, and provide large-scale supervision for data-hungry machine-learning pipelines.
Approach:	They propose a method for synthesizing realistic digital footprints using large language model agents from a structured user profile.
Outcome:	The proposed method generates diverse sequences of user events, producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc.

Controlling Styles in Neural Machine Translation with Activation Prompt (2023.findings-acl)

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Yifan Wang, Zewei Sun, Shanbo Cheng, Weiguo Zheng, Mingxuan Wang

Machine Translation

Challenge:	Earlier studies on controlling styles in neural machine translation (NMT) have focused on regulating the level of formality, but they still encounter two major challenges.
Approach:	They propose a method to control the style of neural machine translation by retrieving prompts from stylized monolingual corpus.
Outcome:	The proposed method can control the style of translation and achieve remarkable performance.

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

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Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

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Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig

Connectivity Patterns are Task Embeddings (2023.findings-acl)

Challenge:	Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information.
Approach:	They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark.
Outcome:	The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%.

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Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, Tao Gui

CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)

Challenge:	Existing methods for predicting inter-task transferability are sparse and task-specific.
Approach:	They propose a method that uses connectivity patterns of neurons as a unique identifier associated with a task.
Outcome:	The proposed method outperforms baselines in predicting inter-task transferability across data regimes and transfer settings while keeping high efficiency in computation and storage.

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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen

Subtopic-driven Multi-Document Summarization (D19-1)

Challenge:	Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected.
Approach:	They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans .
Outcome:	The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test .

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Xin Zheng, Aixin Sun, Jing Li, Karthik Muthuswamy

Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)

Challenge:	Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets.
Approach:	They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (2024.findings-naacl)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks.
Approach:	They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations.
Outcome:	The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks.

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Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, Jing Zheng

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)

Challenge:	Existing approaches to enable large language models to implement function calling are limited in their tool-use capabilities.
Approach:	They propose a controllable, target-driven approach to empower LLMs to operate external APIs only via prompts.
Outcome:	The proposed approach limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion.

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Fanchao Qi, Chuancheng Lv, Zhiyuan Liu, Xiaojun Meng, Maosong Sun, Hai-Tao Zheng

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

Challenge:	Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes.
Approach:	They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes .
Outcome:	The proposed model outperforms previous methods in terms of MAP and F1 scores.

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

Robustness in NLP

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

ChID: A Large-scale Chinese IDiom Dataset for Cloze Test (P19-1)

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Chujie Zheng, Minlie Huang, Aixin Sun

Challenge:	cloze-style reading comprehension in Chinese is limited due to the lack of various corpora.
Approach:	They propose a large-scale Chinese cloze test dataset ChID which studies the comprehension of idiom in Chinese.
Outcome:	The proposed dataset compares the performance of the proposed model with human models.

Towards Imperceptible Document Manipulations against Neural Ranking Models (2023.findings-acl)

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Xuanang Chen, Ben He, Zheng Ye, Le Sun, Yingfei Sun

Robustness in NLP

Challenge:	Current approaches to detect vulnerabilities in neural ranking models often introduce noticeable errors and require a well-imitated surrogate NRM to guarantee the attack effect.
Approach:	They propose a framework called Imperceptible DocumEnt Manipulation to produce adversarial documents that are less noticeable to both algorithms and humans.
Outcome:	The proposed framework outperforms strong baselines while maintaining fluency and correctness of the target documents.

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

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

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

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

Low-Resource NLP

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

RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER (2020.coling-main)

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Lin Sun, Jiquan Wang, Yindu Su, Fangsheng Weng, Yuxuan Sun, Zengwei Zheng, Yuanyi Chen

Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)

Challenge:	Named entity recognition (MNER) for tweets is a key task of many applications.
Approach:	They propose a pre-trained multimodal named entity recognition model based on Relationship Inference and Visual Attention (RIVA) for tweets.
Outcome:	The proposed model improves on the multimodal named entity recognition (MNER) task on tweets with the aid of visual clues.

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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation (2022.emnlp-main)

Challenge:	Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities.
Approach:	They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts.
Outcome:	The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs.

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Yinpei Dai, Wanwei He, Bowen Li, Yuchuan Wu, Zheng Cao, Zhongqi An, Jian Sun, Yongbin Li

Improving Chinese Pop Song and Hokkien Gezi Opera Singing Voice Synthesis by Enhancing Local Modeling (2023.emnlp-main)

Challenge:	a new benchmark for goal-oriented dialog evaluation is needed to address the problem of knowledge sources, noisy user expressions, and the shortage of annotated data.
Approach:	They propose a Chinese benchmark for goal-oriented dialog evaluation that uses dialog sessions and 574,949 dialog turns to bridge the gap between academic benchmarks and spoken dialog scenarios.
Outcome:	The proposed benchmark contains 96,763 dialog sessions and 574,949 dialog turns totally.

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Peng Bai, Yue Zhou, Meizhen Zheng, Wujin Sun, Xiaodong Shi

Speech & Audio in NLP

Challenge:	Singing Voice Synthesis (SVS) synthesizes pleasing vocals based on music scores and lyrics . current acoustic models ignore the significance of local modeling within the sequence and the hard-to-synthesize parts in the predicted mel-spectrogram .
Approach:	They propose a method to enhance local modeling in the acoustic model by focusing on phoneme tokens located before and after the phoneme.
Outcome:	The proposed method improves local modeling in the acoustic model by focusing on the hard-to-synthesize parts of the predicted mel-spectrogram.

CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code (2024.findings-emnlp)

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Batu Guan, Yao Wan, Zhangqian Bi, Zheng Wang, Hongyu Zhang, Pan Zhou, Lichao Sun

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown significant potential in code generation, but they also present challenges regarding the protection of Intellectual Property (IP) related to model architectures, weights, and training data.
Approach:	They propose a multi-bit watermarking technique that embeds additional information to preserve provenance details, such as the vendor ID of an LLM.
Outcome:	The proposed technique preserves provenance details while maintaining syntactical correctness of generated code.

BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)

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Ran Xin, Chenguang Xi, Jie Yang, Feng Chen, Hang Wu, Xia Xiao, Yifan Sun, Shen Zheng, Ming Ding

WebOlympus: An Open Platform for Web Agents on Live Websites (2024.emnlp-demo)

Challenge:	Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored.
Approach:	They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases.
Outcome:	The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods.

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Boyuan Zheng, Boyu Gou, Scott Salisbury, Zheng Du, Huan Sun, Yu Su

GRADA: Graph-based Reranking against Adversarial Documents Attack (2025.emnlp-main)

Challenge:	Web agents are emerging as powerful tools for automating tasks in cyberspace . however, there is a lack of standardized and user-friendly tools for research and development .
Approach:	They propose an open platform for web agents operating on live websites with a Chrome extension and a safety monitor module to ensure their trustworthiness.
Outcome:	WebOlympus is an open platform for web agents operating on live websites.

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Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong, Xuanli He, Pasquale Minervini, Youcheng Sun, Qiongkai Xu

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Retrieval Augmented Generation (RAG) frameworks are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarially similar to the query.
Approach:	They propose a framework that integrates external retrieval modules into RAG frameworks to improve the factual accuracy of large language models.
Outcome:	The proposed framework reduces adversarial attacks by 80% while maintaining minimal loss in accuracy.

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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

Syntactic Text Processing

Challenge:	Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
Approach:	They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.
Outcome:	The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development.

Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)

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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen

Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)

Challenge:	Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples.
Approach:	They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition.
Outcome:	The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses.

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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun

Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)

Challenge:	Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated.
Approach:	They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework.
Outcome:	The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs.

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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu

Text Generation

Challenge:	Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain.
Approach:	They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words.
Outcome:	The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.

MMChat: Multi-Modal Chat Dataset on Social Media (2022.lrec-1)

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Yinhe Zheng, Guanyi Chen, Xin Liu, Jian Sun

Demystify the Role of Memory in Machine Learning Engineering Agents (2026.findings-acl)

Challenge:	Incorporating multi-modal contexts in conversation is important for developing engaging dialogue systems.
Approach:	They propose a large scale Chinese multi-modal dialogue corpus that contains image-grounded dialogues from real conversations on social media.
Outcome:	The proposed model can handle sparsity issues in dialogue generation tasks by incorporating image features.

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Xinyu Zhao, Junpeng Wang, Yuzhong Chen, Menghai Pan, Chin-Chia Michael Yeh, Jiarui Sun, Yan Zheng, Mahashweta Das, Tianlong Chen

Challenge:	Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success.
Approach:	They propose a dynamic coding memory that captures and reuses debugging experiences and integrates it into two representative agent paradigms.
Outcome:	The proposed agent model captures and reuses debugging experiences and integrates it into two agent paradigms.

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong

PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)

Challenge:	Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance.
Approach:	They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process.
Outcome:	Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models.

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Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)

Challenge:	Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence.
Approach:	They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides.
Outcome:	The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides .

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Ziyang Miao, Qiyu Sun, Jingyuan Wang, Yuchen Gong, Yaowei Zheng, Shiqi Li, Richong Zhang

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models (2024.acl-long)

Challenge:	Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents.
Approach:	They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface.
Outcome:	The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs.

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Longze Chen, Ziqiang Liu, Wanwei He, Yinhe Zheng, Hao Sun, Yunshui Li, Run Luo, Min Yang

ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data (2025.findings-acl)

Challenge:	Long-context modeling capabilities are important for large language models (LLMs) however, training LLMs with long context windows is insufficient since some samples do not exhibit strong semantic dependencies across long contexts.
Approach:	They propose a data mining framework ProLong that assigns each training sample with a long dependency score and ranks and filters them according to their results.
Outcome:	The proposed framework can rank and filter training samples that exhibit more powerful long-context modeling abilities.

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Xinzhe Zheng, Sijie Ji, Jiawei Sun, Renqi Chen, Wei Gao, Mani Srivastava

CoLAKE: Contextualized Language and Knowledge Embedding (2020.coling-main)

Challenge:	Existing methods for mental health risk assessment rely on subjective textual records . however, these uncertainties can cause inconsistent and unreliable predictions .
Approach:	They propose a method that integrates objective behavior data alongside subjective mental records for robust mental health risk assessment.
Outcome:	The proposed approach achieves significant improvements over general LLMs.

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Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuanjing Huang, Zheng Zhang

Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)

Challenge:	Existing models for integrating factual knowledge into pre-trained language models are shallow, static, and separately pre-train entities.
Approach:	They propose a method which integrates knowledge contexts from large-scale knowledge bases into a unified data structure.
Outcome:	The proposed model outperforms existing models on knowledge-driven tasks and knowledge probing tasks.

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Qiyuan Zhang, Yufei Wang, Tianhe Wu, Can Xu, Qingfeng Sun, Kai Zheng, Xue Liu, Chen Ma

Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)

Challenge:	Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation.
Approach:	They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline.
Outcome:	The proposed framework surpasses open-source RMs by an average of 8.2%.

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Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li

Sentiment Analysis

Challenge:	Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples.
Approach:	They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors.
Outcome:	The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods.

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

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

CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data (2026.acl-long)

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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Zeyu Zhang, Kexuan Sun, Zheng Tang, Jens-S. Vöckler, Thien Huu Nguyen, Thuy Vu

Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2025.acl-long)

Challenge:	Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress.
Approach:	They propose a knowledge Graph retrieval tool that can translate natural language questions into structured queries.
Outcome:	Extensive experiments show that CypherSmith achieves state-of-the-art performance.

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Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, Bo Zheng

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Challenge:	Current frontier models sometimes generate false outputs or answers that are not substantiated by evidence.
Approach:	They propose Chinese SimpleQA, a Chinese benchmark to evaluate LLMs' factuality . they focus on Chinese language over 6 major topics with 99 diverse subtopics .
Outcome:	The Chinese SimpleQA benchmark evaluates the factuality ability of LLMs . the questions and answers are short and easy-to-evaluate .

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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

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

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

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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

Low-Resource NLP

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.

BeamLoRA: Beam-Constraint Low-Rank Adaptation (2025.acl-long)

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Naibin Gu, Zhenyu Zhang, Xiyu Liu, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, Haifeng Wang

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) is one of the most efficient parameter-efficient fine-tuning methods.
Approach:	They propose to conceptualize each LoRA module as a beam where each rank corresponds to a potential sub-solution.
Outcome:	The proposed method improves performance on three base models and 12 datasets.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Jiawen Deng, Jingyan Zhou, Hao Sun, Chujie Zheng, Fei Mi, Helen Meng, Minlie Huang

Ethical NLP

Challenge:	Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models.
Approach:	They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset.
Outcome:	The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources.

PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support (2021.findings-acl)

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Hao Sun, Zhenru Lin, Chujie Zheng, Siyang Liu, Minlie Huang

CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing research on text-based mental health counseling is limited due to the lack of relevant corpora in Chinese language.
Approach:	They propose a Chinese dataset of psychological health support in the form of question and answer pair that is crawled from a mental health service platform and contains 22K questions and 56K long and wellstructured answers.
Outcome:	The proposed dataset contains 22K questions and 56K long and wellstructured answers.

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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored.
Approach:	They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China.
Outcome:	The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection (2026.findings-acl)

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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Runze Sun, Yu Zheng, Zexuan Xiong, Zhongjin Qu, Lei Chen, Jie Zhou, Jiwen Lu

Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)

Challenge:	Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities.
Approach:	They propose a framework to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on H-VLI and on established benchmarks.

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Rongyi Sun, Ziran Li, Yifeng Ding, Qifan Wang, Jingang Wang, Haitao Zheng, Wei Wu, Yunsen Xian

Can Language Models Understand Physical Concepts? (2023.emnlp-main)

Challenge:	Existing approaches to vision-and-language pretraining (VLP) lack effectiveness and efficiency in downstream multimodal tasks.
Approach:	They propose a flexible vision-and-language pre-training model by incorporating cross-modal fusions into a dual-encoder architecture and a cross-module knowledge transfer strategy to guide the training process.
Outcome:	The proposed model is well-equipped with effectiveness and efficiency compared with other strong VLP models.

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Lei Li, Jingjing Xu, Qingxiu Dong, Ce Zheng, Xu Sun, Lingpeng Kong, Qi Liu

A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)

Challenge:	Existing language models do not understand basic physical concepts in the human world.
Approach:	They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world .
Outcome:	The proposed method achieves comparable performance with scaling up parameters of LMs 134.

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Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li

Open Information Extraction

Challenge:	Existing OIE (Open Information Extraction) algorithms are redundant and not reusable.
Approach:	They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies.
Outcome:	The proposed pipeline provides a platform for all OIE strategies.

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing (2025.emnlp-main)

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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Zhisheng Zheng, Puyuan Peng, Anuj Diwan, Cong Phuoc Huynh, Xiaohang Sun, Zhu Liu, Vimal Bhat, David Harwath

Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming (2026.acl-long)

Challenge:	Autoregressive language model for multilingual speech editing and zero-shot text-to-speech synthesis is available in 11 languages.
Approach:	They introduce an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot text-to-speech synthesis across 11 languages.
Outcome:	The model generates high-quality, natural-sounding speech, even with limited per-language data . it shows robust performance in diverse linguistic settings, even in limited per language data compared to other models .

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Yuhao Li, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang, Huazheng Wang, Zirui Zhuang, Qi Qi, Jianxin Liao, Jingyu Wang

NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)

Challenge:	Existing approaches to code generation fail to consider the quality of retrieved examples.
Approach:	They propose a retrieval-augmented generation method that combines existing API examples to improve complexity and readability.
Outcome:	The proposed method achieves up to 22% accuracy improvement over baseline methods.

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Yi Sun, Yu Zheng, Chao Hao, Hangping Qiu

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)

Challenge:	Recent studies have shown that using prompts to utilize language models to perform downstream tasks is more effective than using token-level methods such as PET.
Approach:	They propose to use a BERT original pre-training task abandoned by RoBERTa and other models to construct a sentence-level prompt-based method that does not need to fix the length of the prompt or the position to be predicted.
Outcome:	The proposed method performs better than PET and EFL on a BERT pre-training task and is comparable to other prompt-based methods.

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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang