Papers by Zhong Li

Revisiting Knowledge Distillation for Autoregressive Language Models (2024.acl-long)

Challenge:	Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance.
Approach:	They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem.
Outcome:	The proposed framework can achieve significant performance gains on discriminative and generative PLMs.

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Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao

FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2025.findings-naacl)

Challenge:	Autoregressive language models (LMs) are expensive and memory intensive, preventing the development of industrial applications.
Approach:	They propose an adaptive teaching approach to improve the KD of autoregressive language models by distilling knowledge into a small student model.
Outcome:	The proposed method can achieve consistent and significant performance gains across all model types and sizes.

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Xinping Zhao, Yan Zhong, Zetian Sun, Xinshuo Hu, Zhenyu Liu, Dongfang Li, Baotian Hu, Min Zhang

Green & Sustainable NLP

Challenge:	Retrieval-Augmented Generation (RAG) is widely adopted in Large Language Models, but is flat and has limitations such as a significant burden on one retriever and constant granularity limits the ceiling of retrieval performance.
Approach:	They propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency.
Outcome:	The proposed paradigm achieves comparable retrieval performance while the time overhead is reduced by nearly 40%.

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification (2024.emnlp-main)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama

HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment (2026.findings-acl)

Challenge:	Recent advances in fine-tuning Vision-Language Models have seen the success of prompt tuning and adapter tuning.
Approach:	They propose a method to fine-tune CLIP without introducing any overhead of extra parameters.
Outcome:	The proposed method improves CLIP by 7.27% average harmonic mean accuracy.

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Guorui Li, Dugang Liu, Lei Li, Xing Tang, Zhong Ming

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

Challenge:	Existing methods for enhancing sequential recommendation use long interaction sequences, but they lack the ability to extract user preferences from long sequences.
Approach:	They propose a plugin that integrates LLMs to infer user preferences from interaction sequences.
Outcome:	The proposed algorithms improve user semantic embedding extraction and utilization on three benchmark datasets.

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)

Challenge:	Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations .
Approach:	They propose a framework to synthesize complex charts and reliable reasoning data from scratch.
Outcome:	Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models .

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Guoming Ling, Zhongzhan Huang, Yupei Lin, Junxin Li, Shanshan Zhong, Hefeng Wu, Liang Lin

Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning (2025.acl-long)

Challenge:	Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning.
Approach:	They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy.
Outcome:	The proposed framework improves accuracy and computational cost while reducing generation length by over 22%.

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Shaobo Wang, Xiangqi Jin, Ziming Wang, Jize Wang, Jiajun Zhang, Kaixin Li, Zichen Wen, Zhong Li, Conghui He, Xuming Hu, Linfeng Zhang

CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (D19-1)

Challenge:	Using fine-tuning on task-specific data is essential for large language models to be effective in specialized tasks.
Approach:	They propose a method that leverages few-shot in-context learning with the model to be fine-tuned.
Outcome:	The proposed method outperforms existing methods with a 3.1-point improvement and a 7.4 speedup on the Llama-3-8B-Instruct model using just 10% of the dataset.

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Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, Xiaowei Shen

Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026.acl-long)

Challenge:	Existing methods for aspect-specific sentiment classification are noisy and downgraded performance.
Approach:	They propose a constrained attention network to regularize attention for multi-aspect sentiment analysis by orthogonal regularization on multiple aspects and sparse regularization for each single aspect.
Outcome:	The proposed approach outperforms state-of-the-art methods on two public datasets and extends to multi-task settings.

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Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, Kezhi Li, Qiang Xu

Challenge:	Recent advances in Large Language Models (LLMs) have enhanced capabilities in complex reasoning through step-by-step trace generation.
Approach:	They propose a generative verifier that dynamically allocates compute between rapid fast thinking and deliberative slow thinking.
Outcome:	The proposed solution outperforms GenPRM-32B on ProcessBench while requiring 2.3x fewer TFLOPS and 15x less training data.

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (2021.acl-long)

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Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong

MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)

Challenge:	Existing methods to detect false claims ignore the characteristics of FC-articles . claims are often quoted to describe checked events, providing lexical information . sentence templates to introduce or debunk claims are common across articles, providing pattern information.
Approach:	They propose a model to rerank FC-articles using key sentences and pattern information.
Outcome:	The proposed model outperforms existing methods on two real-world datasets showing that key sentences can be used to predict if an article fact-checks the given claim.

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Shaopeng Tang, Lin Li, Xiaohui Tao, Leqi Zhong, Qing Xie

Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL (2025.coling-main)

Challenge:	Existing models with strong in-house performance may struggle to generalize to diverse expressions.
Approach:	They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet .
Outcome:	The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity.

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Geling Liu, Yunzhi Tan, Ruichao Zhong, Yuanzhen Xie, Lingchen Zhao, Qian Wang, Bo Hu, Zang Li

Programming Languages in NLP

Challenge:	Existing text-to-SQL approaches have overlooked the critical aspect of system robustness.
Approach:	They propose a robust text-to-SQL solution that integrates with LLMs . their method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% .
Outcome:	The proposed solution achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks.

Confusionset-guided Pointer Networks for Chinese Spelling Check (P19-1)

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Dingmin Wang, Yi Tay, Li Zhong

Text Error Correction

Challenge:	Existing methods to detect and fix errors in Chinese are limited due to context.
Approach:	They propose a Confusionset-guided pointer network for Chinese Spell Check task . they propose to use off-the-shelf confusionset to guide character generation .
Outcome:	The proposed model outperforms all competitor models on three human-annotated datasets.

Document-Level Event Factuality Identification via Adversarial Neural Network (N19-1)

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Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Robustness in NLP

Challenge:	Document-level event factuality identification is crucial for discourse understanding in NLP . identifying document-level factual of events requires comprehensive understanding of documents .
Approach:	They propose to construct a corpus annotated with document- and sentence-level event factuality information on English and Chinese texts.
Outcome:	The proposed model outperforms baselines on the constructed corpus.

LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)

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Jian Lang, Rongpei Hong, Meihui Zhong, Kaiju Li, Ting Zhong, Qiang Gao, Fan Zhou

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)

Challenge:	Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions.
Approach:	They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos .
Outcome:	The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks.

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Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL).
Approach:	They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer.
Outcome:	Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks .

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator (2025.emnlp-main)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Liqiang Ming, Sheng-hua Zhong, Yuncong Li

CEM: Machine-Human Chatting Handoff via Causal-Enhance Module (2022.emnlp-main)

Challenge:	Existing methods for Word Sense Disambiguation rely heavily on manually annotated data, which limits coverage and generalization.
Approach:	They propose a framework that leverages large language models as knowledge distillers to build silver-standard WSD corpora by combining generation-based distillation and annotation-based disambiguation.
Outcome:	The proposed framework outperforms existing methods on general-domain benchmarks by 50% on the most challenging test set and by 1000 times fewer parameters.

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Shanshan Zhong, Jinghui Qin, Zhongzhan Huang, Daifeng Li

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

Challenge:	Existing methods to predict chatbot failure ignore causal variables, resulting in cost increasement and prediction bias.
Approach:	They propose a machine-human chatting handoff module that predicts chatbot failure . they use user state and labor cost to correct the prediction bias .
Outcome:	The proposed method improves the performance of existing methods without any elaborate model crafting.

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step by Step (2024.findings-acl)

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

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Li Zhong, Zilong Wang, Jingbo Shang

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

Challenge:	Large language models (LLMs) are leading progress in code generation, but they are underutilized in the literature.
Approach:	They propose a debugging framework that allows LLMs to refine their generated programs with the runtime execution information.
Outcome:	The proposed framework improves the baseline performance by 9.8% across the HumanEval, MBPP, and TransCoder benchmarks.

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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao

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

Challenge:	Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach:	They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome:	The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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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

TermDiffuSum: A Term-guided Diffusion Model for Extractive Summarization of Legal Documents (2025.coling-main)

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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Xiangyun Dong, Wei Li, Yuquan Le, Zhangyue Jiang, Junxi Zhong, Zhong Wang

MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset (2021.emnlp-main)

Challenge:	Recent studies have explored diffusion models for extractive summarization task, showcasing their remarkable capabilities.
Approach:	They propose a term-guided diffusion model for extractive summarization of legal documents that incorporates legal terminology into the model via a well-designed multifactor fusion noise weighting schedule.
Outcome:	The proposed model outperforms existing models on a self-constructed legal summarization dataset and achieves improvements of 3.10, 2.84, and 2.89 on three public datasets.

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Jing Li, Shangping Zhong, Kaizhi Chen

Natural Language Interfaces

Challenge:	Question Answering (QA) is a branch of QA that enables effective perceiving, accessing, and understanding complex biomedical knowledge by innovative applications.
Approach:	They present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset . they implement eight representative control methods and open-domain QA methods as baselines .
Outcome:	The proposed dataset is the largest-scale Chinese multi-choice biomedical QA dataset . it covers the following biomedically-relevant sub-fields: Clinic, Stomatology, Public Health, Traditional Chinese Medicine, and Traditional Chinese medicine Combined with Western Medicine.

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)

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Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Instruct and Extract: Instruction Tuning for On-Demand Information Extraction (2023.emnlp-main)

Challenge:	Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP.
Approach:	They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks .
Outcome:	The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus .

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Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, Jiawei Han

NarGINA: Towards Accurate and Interpretable Children’s Narrative Ability Assessment via Narrative Graphs (2025.findings-acl)

Challenge:	Large language models with instruction-following capabilities are not suitable for long-tail ad hoc extraction use cases for non-expert users.
Approach:	They propose a task that follows instructions to extract the desired content from the associated text and present it in a structured tabular format.
Outcome:	The proposed paradigm outperforms existing open-source models of similar size in terms of information extraction.

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Jun Zhong, Longwei Xu, Li Kong, Xianzhuo Li, Dandan Liang, Junsheng Zhou

Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph (2026.findings-acl)

Challenge:	Existing methods for assessing children's narrative ability are limited to evaluating completeness of narrative content and the coherence of expression, as well as interpretability of assessment results.
Approach:	They propose a computational framework for assessing narrative ability using a narrative graph to provide a concise and structured summary representation of narrative text.
Outcome:	The proposed framework achieves significant performance improvement over baselines while possessing good interpretability.

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Jianpeng Hu, Yanzeng Li, Jialun Zhong, Lei Zou, Wenfa Qi

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)

Challenge:	Existing methods for detecting faithfulness hallucinations are coarse or do not capture the models’ internal reasoning processes, making it difficult to learn.
Approach:	They propose a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination using Large language models.
Outcome:	The proposed method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.

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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin

Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation (2023.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation .
Approach:	They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input.
Outcome:	The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions.

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Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li

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

Challenge:	Recent success of natural language processing (NLP) is driven by the adoption of large-scale pretrained language models.
Approach:	They propose a method to determine the impact of distillation influence on student generalization ability by prioritizing samples likely to enhance the student's generalization abilities.
Outcome:	The proposed method outperforms 10 common knowledge distillation baselines on 6 text classification tasks in the GLUE benchmark.

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

Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis (2020.emnlp-main)

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

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Yuncong Li, Cunxiang Yin, Sheng-hua Zhong, Xu Pan

P²Net: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts (2025.findings-acl)

Challenge:	Existing methods to detect sentiment toward aspect categories ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance.
Approach:	They propose a multi-instance multi-label learning network for Aspect-Category sentiment analysis that treats sentences as bags, words as instances, and the words indicating an aspect category as key instances of the aspect category.
Outcome:	The proposed model is based on three public datasets showing that it performs well.

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Kaiwen Wei, Jie Yao, Jiang Zhong, Yangyang Kang, Jingyuan Zhang, Changlong Sun, Xin Zhang, Fengmao Lv, Li Jin

KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)

Challenge:	Existing methods for key information extraction are based on a limited set of entity categories and fixed layouts.
Approach:	They propose a large-scale, human-annotated dataset for key information extraction . it is based on a human-annotated layout and 1,162 entity categories . they propose 'parallel pointer-based network' that leverages implicit relationships .
Outcome:	Experiments on widely-used datasets show that the proposed model outperforms state-of-the-art methods while maintaining fast inference speeds.

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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu

Green & Sustainable NLP

Challenge:	Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts.
Approach:	They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs.
Outcome:	The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks.

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)

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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)

Challenge:	Massive open online courses (MOOCs) are a popular educational platform for advanced research.
Approach:	They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource.
Outcome:	The proposed datasets show that they can facilitate research in MOOCs.

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Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong

Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

Challenge:	Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs.
Approach:	They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them.
Outcome:	Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks.

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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach:	They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome:	The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Dugang Liu, Weihao Du, Lei Li, Weike Pan, Zhong Ming

Challenge:	Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency.
Approach:	They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling.
Outcome:	The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets.

Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective (2025.findings-acl)

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Yipeng Kang, Junqi Wang, Yexin Li, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Tingjun Wu, Xue Feng, Fangwei Zhong, Zilong Zheng

Structured Data in NLP

Challenge:	Current approaches to value alignment focus on a few core values, such as helpfulness, harmlessness, and honesty.
Approach:	They propose to use latent causal value graphs to guide two lightweight value-steering methods . role-based prompting and sparse autoencoder (SAE) steering are also used .
Outcome:	Experiments on Gemma-2B-IT and Llama3-8B- IT show that the proposed methods are effective and controllable.

A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022.coling-1)

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Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, Dacheng Tao

SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation (2024.emnlp-main)

Challenge:	Aspect-based sentiment analysis is sensitive to multi-aspect challenges, resulting in multiple aspects in a sentence.
Approach:	They propose a framework that leverages an in-domain generator to construct more multi-aspect samples . they then boost the robustness of ABSA models via contrastive learning on these generated samples ."
Outcome:	The proposed framework outperforms baselines without any augmentations on accuracy and Macro- F1 . the proposed framework can generate more multi-aspect samples and boost the robustness of ABSA models .

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Xinping Zhao, Dongfang Li, Yan Zhong, Boren Hu, Yibin Chen, Baotian Hu, Min Zhang

Information Retrieval

Challenge:	Existing methods for enhancing RAG performance rely on heuristic-based augmentation . Existing approaches rely heavily on a heuriistic-driven approach, resulting in poor generalization and skews in the evidence length.
Approach:	They propose a model-based evidence extraction learning framework that optimizes a vanilla model as an evidence extractor with desired properties through self-aligned learning.
Outcome:	The proposed method reduces the evidence length by 9.25 times and improves reliability and reliability.

Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models (2025.coling-main)

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Kaiwen Wei, Jiang Zhong, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Li Jin, Yue Yu, Jingyuan Zhang

One Token Is Enough: Improving Diffusion Language Models with a Sink Token (2026.findings-acl)

Challenge:	Existing approaches to enhancing large language models fail to emphasize specific constraints and unlock the underlying knowledge.
Approach:	They propose a method that emphasizes specific constraints and unlocks knowledge within LLMs by iteratively emphasising on specific constraints.
Outcome:	The proposed method outperforms existing methods in enhancing generated content, especially in terms of specificity.

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Zihou Zhang, Zheyong Xie, Li Zhong, Haifeng Liu, Shaosheng Cao

Privacy Implications of Retrieval-Based Language Models (2023.emnlp-main)

Challenge:	Existing Diffusion Language Models lack a structural constraint to stabilize attention sinks.
Approach:	They propose a simple but effective extra sink token that is constrained to attend to itself while remaining globally visible to all other tokens.
Outcome:	The proposed token is able to stabilize attention sinks and improve model performance.

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Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li, Danqi Chen

Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)

Challenge:	a study of retrieval-based language models shows improved interpretability, factuality, and adaptability compared to parametric counterparts . kNN-LMs are more susceptible to leaking private information from their private datastore than parametric models .
Approach:	They present the first study of privacy risks in retrieval-based language models . they aim to strike a balance between utility and privacy in domains where privacy is of concern .
Outcome:	The proposed methods improve interpretability, factuality, and adaptability compared to parametric models . the study finds that kNN-LMs are more susceptible to leaking private data than parametric ones .

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Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, Tat-Seng Chua

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)

Challenge:	Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges .
Approach:	They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs.
Outcome:	The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable.

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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao

Low-Resource NLP

Challenge:	Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial.
Approach:	They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training.
Outcome:	The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training.

Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)

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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang

Challenge:	Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions.
Approach:	They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process .
Outcome:	The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures.

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)

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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion (2024.emnlp-main)

Challenge:	Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor.
Approach:	They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level.
Outcome:	The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors.

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Qingyang Li, Yanru Zhong, Yuchu Qin

Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games (2026.findings-acl)

Challenge:	Existing approaches to knowledge graph completion have not integrated the structural attributes of knowledge graphs with the textual descriptions of entities to generate robust entity encodings.
Approach:	They propose to integrate structural information from knowledge graphs with textual descriptions of entities to generate robust entity encodings.
Outcome:	The proposed model improves on the standard evaluation metric, Mean Reciprocal Rank (MRR), while surpassing the current best model on the Wikidata5M dataset.

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Keyang Zhong, Junlin Xie, Hefeng Wu, Haofeng Li, Guanbin Li

Large Language Models as Reader for Bias Detection (2025.findings-emnlp)

Challenge:	Vision-language models have shown impressive capabilities in perceptual tasks . however, they degrade in complex multi-hop reasoning under multi-player game settings .
Approach:	They propose a multi-agent framework for evaluating and synthesizing role-driven game scripts . they use curated and synthetic datasets to model uncertainty and deception .
Outcome:	The proposed model significantly boosts the performance of VLMs in narrative reasoning and hidden fact extraction under uncertain, adversarial, and socially complex conditions.

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Xuan Luo, Jing Li, Zhong Wenzhong, Geng Tu, Ruifeng Xu

CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models (2024.findings-emnlp)

Challenge:	Traditional methods analyze text from the writer’s perspective, leaving the reader’s viewpoint underexplored.
Approach:	They investigate whether large language models can be leveraged as readers for bias detection by generating reader-perspective comments.
Outcome:	The proposed model performs comparable to GPT4's in detecting bias in media content.

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Zexuan Qiu, Jingjing Li, Shijue Huang, Xiaoqi Jiao, Wanjun Zhong, Irwin King

Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)

Challenge:	Developing long-context LLMs with robust long-text capabilities is underdeveloped due to a lack of benchmarks.
Approach:	They propose a Chinese benchmark for evaluating long-context LLMs with Chinese capabilities.
Outcome:	The proposed model is based on 6 open-source LLMs and 2 commercial ones.

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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu

Challenge:	Positional biases in large language models hinder their ability to process long inputs.
Approach:	They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information.
Outcome:	The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks.

The Devil in Linear Transformer (2022.emnlp-main)

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Zhen Qin, Xiaodong Han, Weixuan Sun, Dongxu Li, Lingpeng Kong, Nick Barnes, Yiran Zhong

From Personal to Collective: On the Role of Local and Global Knowledge in LLM Personalization (2026.findings-acl)

Challenge:	Existing linear transformers suffer from performance degradations on various tasks and corpus.
Approach:	They propose a new linear attention that replaces scaling with a normalization to stabilize gradients and confine attention to neighbouring tokens in early layers.
Outcome:	The proposed model outperforms vanilla transformers on the long-range arena benchmark while being significantly more space-time efficient.

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Zehong Wang, Junlin Wu, Zhaoxuan Tan, Bolian Li, Xianrui Zhong, Zheli Liu, Qingkai Zeng

Challenge:	Large language models (LLMs) are the backbone of modern AI systems supporting a wide range of applications.
Approach:	They propose a Local–Global knowledge framework that augments user-specific signals with a global knowledge encoding collective behavioral trends.
Outcome:	The proposed framework outperforms existing methods on five personalization benchmarks.

Incorporating Factuality Inference to Identify Document-level Event Factuality (2023.findings-acl)

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Heng Zhang, Peifeng Li, Zhong Qian, Xiaoxu Zhu

Challenge:	Document-level Event Factuality Identification (DEFI) is a task for identifying the degree of certainty that a specific event occurs in a document.
Approach:	They propose a document-level event factuality inference task to bridge the sentence-level and document- level event factualality semantically.
Outcome:	The proposed model outperforms the baselines on the public English and Chinese DLEF datasets.

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

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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

Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)

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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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li

Information Retrieval

Challenge:	Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge.
Approach:	They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage.
Outcome:	The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold.

FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (2024.acl-long)

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Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang

DSRAG: A Double-Stream Retrieval-Augmented Generation Framework for Countless Intent Detection (2025.naacl-industry)

Challenge:	Existing benchmarks focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction.
Approach:	They propose a Multi-level Fine-grained Constraints Following Benchmark for Large Language Models that adds a single constraint to the initial instruction at each increased level.
Outcome:	The proposed model can follow instructions with more constraints, and is deemed to have better instruction-following ability.

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Pei Guo, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li

Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)

Challenge:	Current intent detection work experiments with minor intent categories.
Approach:	They propose a retrieval-augmented generation framework that uses query-to-query and query- to-metadata approaches to retrieve intents from metadata.
Outcome:	The proposed framework improves on query-to-query (Q2Q) and query- to-metadata (Q 2M) approaches.

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Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying Wei, Defu Lian, Zhendong Mao

Text Generation

Challenge:	Existing MCTG methods face a noticeable performance drop in compositional testing.
Approach:	They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol.
Outcome:	The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing.

Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)

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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun

BlockPruner: Fine-grained Pruning for Large Language Models (2025.findings-acl)

Challenge:	Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas.
Approach:	They propose to use 40K different JSON schemas to assess models' ability to generate valid JSON outputs.
Outcome:	The proposed model improves both in generating JSON outputs and downstream tasks.

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Longguang Zhong, Fanqi Wan, Ruijun Chen, Xiaojun Quan, Liangzhi Li

Scaling Laws for Linear Complexity Language Models (2024.emnlp-main)

Challenge:	Large language models (LLMs) have significant computational and memory costs associated with training and inference.
Approach:	They propose a training-free structured pruning approach that targets redundancies in MHA and MLP blocks.
Outcome:	The proposed pruning approach achieves more granular and effective pruning compared to state-of-the-art pruning methods.

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Xuyang Shen, Dong Li, Ruitao Leng, Zhen Qin, Weigao Sun, Yiran Zhong

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation (2026.acl-long)

Challenge:	Existing scaling laws for large language models are unclear, but they are useful for scalability.
Approach:	They propose scaling laws for linear complexity language models to establish a foundation for their scalability.
Outcome:	The proposed models demonstrate superior linguistic proficiency and knowledge retention.

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Kaiwen Wei, Kejun he, Xiaomian Kang, Jie Zhang, null Ymyang, Li Jin, Zhenyang Li, Jiang Zhong, Richard He Bai, Junnan Zhu

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Generative recommendation models inherently bias towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.
Approach:	They propose a training framework that shifts the objective from simple next-step prediction to deep comprehension of history by entropy-guided masking policy and a curriculum learning scheduler to enhance the framework.
Outcome:	The proposed framework outperforms state-of-the-art generative models on three public datasets and shows that it is more accurate than current models.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Trucidator: Document-level Event Factuality Identification via Hallucination Enhancement and Cross-Document Inference (2025.coling-main)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Zihao Zhang, Zhong Qian, Xiaoxu Zhu, Peifeng Li, Qiaoming Zhu

FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models (2025.emnlp-main)

Challenge:	Document-level event factuality identification (DEFI) assesses the veracity degree to which an event mentioned in a document has happened.
Approach:	They propose a document-level event factuality identification framework with hallucination features . they propose factualusion corpus that integrates both genuine and hallucinous false information .
Outcome:	The proposed framework outperforms baselines in document event factuality identification.

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Hengxing Cai, Jinhan Dong, Jingjun Tan, Jingcheng Deng, Sihang Li, Zhifeng Gao, Haidong Wang, Zicheng Su, Agachai Sumalee, Renxin Zhong

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	Existing methods for vision-and-language navigation struggle with insufficient multimodal fusion, weak generalization, and poor interpretability.
Approach:	They propose a framework for UAV vision-and-language navigation that integrates natural language instructions with visual observations to improve multimodal fusion and interpretability.
Outcome:	The proposed framework achieves state-of-the-art performance across all scenarios, with a 9.22% higher success rate than the strongest baseline in unseen environments.

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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang

Text Generation

Challenge:	Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach:	They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome:	The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)

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Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar

FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration (2025.findings-acl)

Challenge:	Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference.
Approach:	They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference.
Outcome:	The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains.

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Zongkai Zhao, Guozeng Xu, Xiuhua Li, Kaiwen Wei, Jiang Zhong

Ethical NLP

Challenge:	Existing methods for updating large language models are inefficient in multi-client scenarios . Existing approaches assume a single-user setting and are ineffective in multiclient scenarios.
Approach:	They propose a new task that enables multiple clients to perform LEKE while preserving privacy and reducing computational overhead.
Outcome:	The proposed framework outperforms existing LEKE frameworks on two benchmark datasets and retains 96% of performance.

EMA: An Episodic Memory Agent for Efficient and Selective Memory (2026.findings-acl)

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Hongyi Lan, Jiaqi Song, Zhengjia Zhong, Hui Li, Hong Liu, Xianming Lin, Rongrong Ji

Dialogue Systems & Conversational Agents

Challenge:	Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency.
Approach:	They propose a framework that abstracts conversational context into Episodic Memory Units (EMUs) they propose EMA, MemDecider and a filtering decision module to reduce noise and improve overall performance.
Outcome:	The proposed framework reduces token consumption by 11.48% while improving performance on two widely-used benchmarks.

Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)

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Shucheng Li, Lingfei Wu, Shiwei Feng, Fangli Xu, Fengyuan Xu, Sheng Zhong

Multilinguality

Challenge:	Graph2Tree model encodes graph-structured input and decodes tree-structures output.
Approach:	They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output.
Outcome:	The proposed model outperforms or matches the performance of other state-of-the-art models on two problems, neural semantic parsing and math word problem.

Dyve: Thinking Fast and Slow for Dynamic Process Verification (2025.emnlp-main)

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Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, Qiang Xu

Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)

Challenge:	Existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels.
Approach:	They propose a dynamic process verifier that integrates fast and slow thinking to enhance reasoning error detection in large language models.
Outcome:	The proposed system outperforms existing process-based verifiers and maintains computational efficiency while maintaining high performance.

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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li

DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models (2026.findings-acl)

Challenge:	Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes.
Approach:	They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy.
Outcome:	The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline.

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Shaokai He, Kaiwen Wei, Xinyi Zeng, Xiang Chen, Xue Yang, Zhenyang Li, Jiang Zhong, Yu Tian

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

Challenge:	Existing large language models exhibit unidirectional behavior when processing bidirectional relationships . authors propose a solution to alleviate the reversal curse in Diffusion LLMs .
Approach:	They propose a model that addresses the "reversal curse" of bidirectional behavior in large language models . they propose 'entity-aware training' and balanced data construction to alleviate asymmetry and missing relations .
Outcome:	The proposed model alleviates the "reversal curse" in Diffusion LLMs . the proposed model employs whole-entity masking to mitigate entity fragmentation .

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

MedDCR: Learning to Design Agentic Workflows for Medical Coding (2026.findings-acl)

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

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Jiyang Zheng, Islam Nassar, Thanh Vu, Xu Zhong, Yang Lin, Tongliang Liu, Long Duong, Yuan-Fang Li

Challenge:	Medical coding is the process of translating unstructured clinical notes into standardized diagnostic and procedural codes.
Approach:	They propose a closed-loop framework that treats workflow design as a learning problem.
Outcome:	The proposed framework outperforms state-of-the-art workflows on benchmark datasets and produces interpretable, adaptable workflows that better reflect real coding practice.

Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)

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Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, Dacheng Tao

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal .
Approach:	They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM.
Outcome:	The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks.

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Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)

Challenge:	Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation.
Approach:	They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities.
Outcome:	The proposed bilingual benchmark assesses models’ language understanding and generation capabilities.

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Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

Challenge:	Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation.
Approach:	They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt.
Outcome:	The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks.

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Programming Languages in NLP

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

Guidelines as Environments: A World Model Approach to Rule Following (2026.acl-long)

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Haiqing Li, Wenliang Zhong, Yinhao Wu, Hehuan Ma, Yuzhi Guo, Thao M. Dang, Junzhou Huang

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

Challenge:	Existing models for guideline-following are a poor fit for ambiguous, text-defined constraints.
Approach:	They propose a Rule-Grounded Causal World Model that builds an explicit state space from guideline text itself.
Outcome:	Experiments show that the proposed model can be used to model rule execution with an explicit state space from the guideline text itself.

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

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models (2025.coling-main)

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

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Li Yang, Zhiding Xiao, Wenxin Huang, Xian Zhong

MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)

Challenge:	Existing models struggle to maintain temporal, spatial, and narrative coherence across image sequences . existing models lack depth and engagement of human-authored stories .
Approach:	They propose a topic-driven narrative optimizer that integrates image descriptions, topic generation, and GPT-4-based refinements.
Outcome:	The proposed framework outperforms existing models in visual relevance, coherence, and fluency.

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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin

Multilinguality

Challenge:	Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures.
Approach:	They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference .
Outcome:	The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks .

Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)

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Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji, Jiawei Han