Papers by Yu Luo

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

Challenge:	Neural machine translation models with deeper neural networks are difficult to train.
Approach:	They propose a MultiScale Collaborative framework to boost gradient back-propagation . they let each encoder block learn a fine-grained representation and enhance it .
Outcome:	The proposed framework outperforms baseline models on translation tasks with three translation directions and achieves a BLEU score of 30.56 on the English-to-German task.

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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

Improving Medical NLI Using Context-Aware Domain Knowledge (2020.starsem-1)

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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Shaika Chowdhury, Philip Yu, Yuan Luo

Knowledge Representation

Challenge:	Domain knowledge is important to understand both the lexical and relational associations of words in natural language text . lack of annotated dataset can lead to wrong inference predictions .
Approach:	They propose a knowledge adaptive approach that encodes the premise/hypothesis texts by leveraging supplementary external knowledge alongside the UMLS based on the word contexts.
Outcome:	The proposed model can align token-level interactions between the premise and hypothesis more effectively.

SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)

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Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)

Challenge:	a growing number of cloud-based inference services are relying on SMPC to protect data privacy.
Approach:	They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance.
Outcome:	The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE .

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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen

FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)

Challenge:	Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods.
Approach:	They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation.
Outcome:	The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs.

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Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies.
Approach:	They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning.
Outcome:	The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states.

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Towards Enhancing Faithfulness for Neural Machine Translation (2020.emnlp-main)

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

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Rongxiang Weng, Heng Yu, Xiangpeng Wei, Weihua Luo

REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)

Challenge:	Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.
Approach:	They propose a training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model.
Outcome:	The proposed model can generate high-quality sentences that are very close to natural language.

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Jianwen Luo, Yu Hong, Shuai Yang, Jianmin Yao

HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)

Challenge:	Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE.
Approach:	They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM)
Outcome:	The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks.

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Xiangfeng Luo, Ruoxin Zheng, Jianqiang Huang, Hang Yu

MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)

Challenge:	Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.
Approach:	They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption.
Outcome:	The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions.

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Jinwen Luo, Jiuding Yang, Weidong Guo, Chenglin Li, Di Niu, Yu Xu

DPC: Training-Free Text-to-SQL Candidate Selection via Dual-Paradigm Consistency (2026.acl-long)

Challenge:	Existing methods for text ranking have improved performance, but there are still challenges.
Approach:	They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix.
Outcome:	The proposed method outperforms all prior methods on datasets with extensive results.

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Boyan Li, Ou Ocean Kun Hei, Yue Yu, Yuyu Luo

Programming Languages in NLP

Challenge:	Existing methods for generating SQL queries lack the ability to self-evaluate correctness without an execution oracle.
Approach:	They propose a framework that reformulates SQL selection from a probabilistic guessing task on hidden data into a deterministic verification task on visible data.
Outcome:	Experiments on BIRD and Spider show that the proposed method outperforms baselines.

Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions (D18-1)

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Qing Li, Jianlong Fu, Dongfei Yu, Tao Mei, Jiebo Luo

Uncertainty-Aware Semantic Augmentation for Neural Machine Translation (2020.emnlp-main)

Challenge:	Existing approaches to visual question answering represent images using pre-trained CNNs . but they rarely provide any insight, apart from the answer, into the VQA process .
Approach:	They propose to break up the end-to-end VQA into two steps: explaining and reasoning . they first extract attributes and generate descriptions as explanations for an image . a reasoning module utilizes these explanations in place of the image to infer an answer .
Outcome:	The proposed system achieves comparable performance with baselines, but with added benefits of explanability and the ability to improve with higher quality explanations.

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Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Luxi Xing, Weihua Luo

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)

Challenge:	Existing methods for neural machine translation only observe one source sentence at training time . this discrepancy in data distribution leads to a formidable learning challenge .
Approach:	They propose an uncertainty-aware semantic augmentation approach to capture universal semantic information among multiple source sentences and enhance hidden representations with this information.
Outcome:	The proposed approach outperforms baseline and existing methods on translation tasks.

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Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam Tu Nguyen

Dialogue Systems & Conversational Agents

Challenge:	Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges.
Approach:	They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains .
Outcome:	The proposed tasks are challenging and worthy of further research.

Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis (2024.findings-emnlp)

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Jianxiang Yu, Zichen Ding, Jiaqi Tan, Kangyang Luo, Zhenmin Weng, Chenghua Gong, Long Zeng, RenJing Cui, Chengcheng Han, Qiushi Sun, Zhiyong Wu, Yunshi Lan, Xiang Li

Self-Supervised Prompt Optimization (2025.findings-emnlp)

Challenge:	Existing approaches to review scientific papers are limited by their content or quality . SEA is a framework for automated scientific review, but its contents are generic or partial.
Approach:	They propose a framework for automated scientific review using large language models . they propose to use a standardized review dataset to fine-tune an LLM to generate high-quality reviews.
Outcome:	The proposed framework can generate high-quality reviews from standardized datasets and improves on the existing feedback mechanisms.

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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo

Low-Resource NLP

Challenge:	Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain.
Approach:	They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference.
Outcome:	The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples.

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)

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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang

Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022.findings-naacl)

Challenge:	Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs.
Approach:	They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them.
Outcome:	The proposed architecture achieves comparable performance with GShard with 2B parameters and computation.

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Huaiyuan Ying, Shengxuan Luo, Tiantian Dang, Sheng Yu

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Existing methods to locate and classify entities using knowledge bases and unlabeled corpus are expensive and limited application.
Approach:	They propose to use a method to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing them in contrastive learning frameworks.
Outcome:	The proposed method can give modified suggestions on distant data without additional supervised labels and thus reduces the requirement on the quality of the knowledge bases.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

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

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive 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

Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026.findings-acl)

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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Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan

Dialogue Systems & Conversational Agents

Challenge:	Recent large reasoning models (LLMs) lack dynamic and diverse thinking capabilities . reusing atomic thoughts provides a practical pathway toward dynamic reasoning .
Approach:	They propose a framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses.
Outcome:	The proposed framework extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses.

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

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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

Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (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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Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu

Challenge:	Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively .
Approach:	They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias .
Outcome:	The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark.

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)

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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu

Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)

Challenge:	Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data.
Approach:	They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs.
Outcome:	The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements .

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Huanhuan Wei, Xiao Luo, Hongyi Yu, Jinping Liang, Luning Yang, Lixing Lin, Alexandra Popa, Xiting Yan

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

Challenge:	Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously.
Approach:	They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts.
Outcome:	The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge.

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang

ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)

Challenge:	Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts.
Approach:	They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning .
Outcome:	Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu

Challenge:	Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates.
Approach:	They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion.
Outcome:	The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings.

Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues (2026.findings-acl)

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Zory Zhang, Pinyuan Feng, Bingyang Wang, Tianwei Zhao, Suyang Yu, Qingying Gao, Hokin Deng, Ziqiao Ma, Yijiang Li, Dezhi Luo

Natural Language Interfaces

Challenge:	Where someone looks is a nonverbal communication cue that children and adults readily use.
Approach:	They used 1,360 real-world photos to construct evaluation stimuli for Vision-Language Models (VLMs) they found a substantial performance gap between VLMs and humans .
Outcome:	The proposed model outperforms existing models in predicting gaze direction using head orientation rather than eye appearance.

MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)

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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along.
Approach:	They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks.
Outcome:	The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks.

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)

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Xinyu Zhang, Pei Zhang, Shuang Luo, Jialong Tang, Yu Wan, Baosong Yang, Fei Huang

Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition (2023.findings-acl)

Challenge:	Cultural competence is defined as the ability to understand and adapt to multicultural contexts.
Approach:	They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs.
Outcome:	The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs.

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Jiuding Yang, Jinwen Luo, Weidong Guo, Di Niu, Yu Xu

Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020.acl-main)

Challenge:	Named Entity Recognition (CNER) is a widely used technology in various applications.
Approach:	They propose a method that uses a custom-designed relevance scoring function to learn the potential relevance between different flattened hierarchical labels.
Outcome:	The proposed method outperforms the state-of-the-art on the FiNE dataset.

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Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)

Challenge:	Existing methods to generate questions for verbose reviews are inefficient for potential consumers . lack of training data hinders efficient review digestion, authors say .
Approach:	They propose to generate questions that can be answered by corresponding review sentences . they propose an iterative learning framework with adaptive instance transfer and augmentation .
Outcome:	The proposed model can generate questions that can be answered by review sentences . it is easier to find critical review parts that are important for potential consumers .

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Guoxin Yu, Jiwei Li, Ling Luo, Yuxian Meng, Xiang Ao, Qing He

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

Challenge:	unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities.
Approach:	They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task .
Outcome:	The proposed framework can solve the ABSA task without any additional data annotation or transformation.

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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

An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection (2022.findings-acl)

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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Shengxuan Luo, Sheng Yu

URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)

Challenge:	Existing methods for knowledge graph integration lack dangling entities that can be manually extracted.
Approach:	They propose a Unsupervised method for joint Entity alignment and Dangling entity detection that uses literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA.
Outcome:	The proposed method outperforms state-of-the-art methods in the EA and DED tasks and achieves comparable results without supervision.

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Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, Yue Yu

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

Challenge:	Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation.
Approach:	They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks.

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

HTMR: Hybrid Token Masking Reinforcement Learning with Verifiable Rewards for Event Argument Extraction with Multi-Perspective Reasoning (2026.acl-long)

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

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Jianwen Luo, Yongkang Jin, Yu Hong, Jianmin Yao

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	Recent work formulates EAE with large language models as a structured conditional generation task and applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize sequence-level event structures.
Approach:	They propose a method that selectively updates policy gradients on high-entropy forking tokens and event-critical tokens that define event structure.
Outcome:	The proposed method outperforms full-token and high-entropy only methods and transfers effectively as a plug-and-play approach to other tasks such as named entity recognition and relation classification.

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision (2024.findings-emnlp)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang

Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)

Challenge:	a method for process supervision has shown significant improvements in multi-step problem solving . despite the advances in process supervision, there are still easily observable mistakes in state-of-the-art LLMs.
Approach:	They propose a method for automating data curation by using a trained verifier to evaluate intermediate steps generated by a reasoner.
Outcome:	The proposed method improves the performance of PaLM 2 on math and coding tasks.

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Xinyu Zuo, Haijin Liang, Ning Jing, Shuang Zeng, Zhou Fang, Yu Luo

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities.
Approach:	They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability .
Outcome:	The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities.

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Probing Semantic Routing in Large Mixture-of-Expert Models (2025.findings-emnlp)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Man Luo, Sungduk Yu, Chendi Xue, Vasudev Lal

Dynamic Scaling of Unit Tests for Code Reward Modeling (2025.acl-long)

Challenge:	large mixture-of-expert models have become increasingly common in the open domain . prior work has explored functional differentiation through routing behavior .
Approach:	They investigate whether expert routing in large mixture-of-expert models is influenced by the semantics of the inputs.
Outcome:	The results show that expert routing is influenced by the semantics of the inputs.

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Zeyao Ma, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, Jie Tang

Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing large language models struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation.
Approach:	They propose a lightweight yet effective unit test generator that scales unit tests based on problem difficulty.
Outcome:	The proposed approach significantly improves performance on three benchmarks.

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Weiqing Luo, Zongye Hu, Xiao Wang, Zhiyuan Yu, Haofeng Zhang, Ziyi Huang

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2025.acl-long)

Challenge:	Existing methods for multimodal retrieval-augmented generation rely on semantic relevance or surface-level similarity, which are often misaligned with the actual utility of visual evidence for downstream reasoning.
Approach:	They propose a latent notion of evidence usefulness and propose 'surrogate-accelerated' framework that efficiently estimates evidence utility using lightweight multimodal models.
Outcome:	The proposed framework outperforms state-of-the-art models while achieving substantial reductions in computational cost.

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Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Mohamed Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo Sapan, Taki Hasan Rafi, Bin Wang, null Supryadi, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Aurora Izzani, Ayushman Singh, Evan Evan, Jauza Akbar Krito, Michael Anugraha, Fenal Ashokbhai Ilasariya, Haochen Li, John Amadeo Daniswara, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Can Udomcharoenchaikit, Fadil Risdian Ansori, Mahardika Krisna Ihsani, Giang Nguyen, Anab Maulana Barik, Dan John Velasco, Rifo Ahmad Genadi, Saptarshi Saha, Chengwei Wei, Isaiah Edri W. Flores, Kenneth Chen Ko Han, Anjela Gail D. Santos, Wan Shen Lim, Kaung Si Phyo, Tim Santos, Meisyarah Dwiastuti, Jiayun Luo, Jan Christian Blaise Cruz, Ming Shan Hee, Ikhlasul Akmal Hanif, M.Alif Al Hakim, Muhammad Rizky Sya’ban, Kun Kerdthaisong, Lester James Validad Miranda, Fajri Koto, Tirana Noor Fatyanosa, Alham Fikri Aji, Jostin Jerico Rosal, Jun Kevin, Robert Wijaya, Onno P. Kampman, Ruochen Zhang, Börje F. Karlsson, Peerat Limkonchotiwat

TVWorld: Foundations for Remote-Control TV Agents (2026.findings-acl)

Challenge:	Southeast Asia is underrepresented in vision-language research . SEA-VL is an open-source initiative dedicated to developing culturally relevant datasets for SEA languages.
Approach:	They propose to use crowdsourced, automated image crawling and synthetic image generation to develop culturally relevant datasets for SEA languages.
Outcome:	The proposed datasets capture SEA cultural nuances and contexts better than existing datasets.

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Zhantao Ma, Quanfeng Lu, Shuai Zhong, Dahai Yu, Ping Luo, Michael Ng

Training Long-Context LLMs Efficiently via Chunk-wise Optimization (2025.findings-acl)

Challenge:	Existing work on large vision–language models focuses on point-and-click interaction, while remote-control interaction is underexplored.
Approach:	They propose a topology-aware training framework that injects topology awareness into LVLMs.
Outcome:	The proposed model achieves 68.3% success rate on TVWorld-N, surpassing closed-source benchmarks and state-of-the-art (SOTA) benchmarks show that existing agents lack topology awareness for focus-based, long-horizon TV navigation.

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Wenhao Li, Yuxin Zhang, Gen Luo, Daohai Yu, Rongrong Ji

Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)

Challenge:	Recent advances in long-context large language models have demonstrated superior retrieval quality compared to retrievalaugmented generation (RAG) approaches.
Approach:	They propose a memory-efficient training paradigm that partitions lengthy inputs into manageable chunks.
Outcome:	The proposed model expands maximum sequence length from 1K to 16K tokens on a single RTX 3090 GPU, while SpaCO achieves accelerated training speed.

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Changfeng Zhu, Heng Yu, Shanbo Cheng, Weihua Luo

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification (2022.emnlp-main)

Challenge:	Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained.
Approach:	They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair.
Outcome:	The proposed model achieves remarkable improvements over state-of-the-art multilingual NMT models and produces comparable performance with strong individual models.

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Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu

TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)

Challenge:	Existing datasets that ignore law requirements are limited to English.
Approach:	They construct a Chinese privacy policy dataset that can be used to analyze software privacy policies.
Outcome:	The proposed dataset includes 483 Chinese Android privacy policies, over 11K sentences, and 52K fine-grained annotations.

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Bo Lv, Xin Liu, Kaiwen Wei, Ping Luo, Yue Yu

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

Challenge:	Large language models (LLMs) have produced impressive results in the field of Multilingual Neural Machine Translation (MNMT).
Approach:	They propose a Teacher Assistant enhanced Knowledge Distillation method to augment knowledge transfer capacity from closed-source MNMT models.
Outcome:	The proposed method outperforms the state-of-the-art KD methods on both WMT22 and FLORES-101 test sets.

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Learning a Grammar Inducer from Massive Uncurated Instructional Videos (2022.emnlp-main)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

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Songyang Zhang, Linfeng Song, Lifeng Jin, Haitao Mi, Kun Xu, Dong Yu, Jiebo Luo

Visual Data in NLP

Challenge:	aims to find more accurate syntactic grammars for accompanying text using video data.
Approach:	They build a video-aided grammar induction model that can learn video-span correlation without manual features.
Outcome:	The proposed model can learn video-span correlation without manual features adopted by previous systems.

Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness (2025.findings-emnlp)

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Yusheng Zhao, Xiao Luo, Junyu Luo, Weizhi Zhang, Zhiping Xiao, Wei Ju, Philip S. Yu, Ming Zhang

Prompt-Based Editing for Text Style Transfer (2023.findings-emnlp)

Challenge:	Multi-modal large language models have been used for processing and understanding information from diverse modalities.
Approach:	They propose to evaluate the audio-visual capabilities of multi-modal large language models . they focus on effectiveness, efficiency, generalizability, and robustness .
Outcome:	The proposed models exhibit strong zero-shot and few-shot generalization abilities . their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing.

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Guoqing Luo, Yu Han, Lili Mou, Mauajama Firdaus

Low-Resource NLP

Challenge:	Text style transfer is a type of textual prompt that generates style-transferred texts word by word . early prediction errors may affect future word predictions.
Approach:	They propose a prompt-based editing approach to text style transfer using a pretrained language model.
Outcome:	The proposed approach outperforms existing systems with 20 times more parameters on three style-transfer benchmark datasets.

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)

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Zhiyin Yu, Bo Zhang, Qibin Hou, Zhonghai Wu, Xiao Luo, Lei Bai

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)

Challenge:	Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking.
Approach:	They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data.
Outcome:	The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data.

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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Challenge:	Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses.
Approach:	They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times .
Outcome:	CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance.

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Jinpeng Li, Hang Yu, Xiangfeng Luo, Qian Liu

Structured Data in NLP

Challenge:	Existing methods for knowledge graph completion (KGC) are limited in generality and scalability due to poor contextual facts.
Approach:	They propose a contextual facts collector and contextual facts organizer to enhance the inference ability of GM-based methods for various KGC tasks.
Outcome:	The proposed model outperforms state-of-the-art methods in terms of performance.

Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark (2026.findings-acl)

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Zihan Luo, Hong Huang, Jianxun Lian, Yu Chang, Xing Xie, Hai Jin

Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are fundamentally reshaping the scientific landscape, transitioning the role of AI from passive tools to active partners within a new paradigm of Human-AI collaboration.
Approach:	They propose a benchmark to evaluate the ability of Large Language Models to improve papers with human feedback.
Outcome:	The proposed benchmark tests the skills of Large Language Models (LLMs) on paper interpretation, experimental implementation, and paper formulation, using authors’ camera-ready versions as natural human baselines.

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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang

Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

Challenge:	Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models.
Approach:	This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy .
Outcome:	The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications.

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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo

Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)

Challenge:	Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach:	They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome:	The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.

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Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li, Qing He

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

Challenge:	Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages .
Approach:	They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA.
Outcome:	The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate.

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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

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)

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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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen

WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding.
Approach:	They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations.
Outcome:	The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks.

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Xingyu Chen, Zihan Zhao, Lu Chen, JiaBao Ji, Danyang Zhang, Ao Luo, Yuxuan Xiong, Kai Yu

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Using a web page and a question, a machine can't understand the contents of web pages.
Approach:	They propose a novel dataset for web-based structural reading comprehension that consists of 400K question-answer pairs and a dataset of 6.4K web pages.
Outcome:	The proposed dataset consists of 400K question-answer pairs, collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata.

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation (2022.acl-long)

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

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Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Weihua Luo, Rong Jin

Machine Translation

Challenge:	Neural machine translation (NMT) tasks require large amounts of parallel data to augment training.
Approach:	They propose a data augmentation paradigm that augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning.
Outcome:	The proposed paradigm improves on the state-of-the-art in supervised neural machine translation tasks.

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

RanLoRA: Residual-aware Nonlinear Low-Rank Adaptation (2026.findings-acl)

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

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Xu Luo, Yongbin Liu, Chunping Ouyang, Ying Yu

Federated Retrieval Augmented Generation for Multi-Product Question Answering (2025.coling-industry)

Challenge:	Low-Rank Adaptation (LoRA) relying on linear low-rank projections restricts adaptation to linear subspaces, limiting flexibility on complex downstream tasks.
Approach:	They propose a nonlinear low-rank Adaptation approach that leverages pretrained weights to decompose them into principal components that are kept frozen and residual components that can be used for task-specific adaptation.
Outcome:	The proposed approach outperforms vanilla LoRA and representative variants on commonsense reasoning, image classification, and mathematical reasoning tasks.

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Parshin Shojaee, Sai Sree Harsha, Dan Luo, Akash Maharaj, Tong Yu, Yunyao Li

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

Challenge:	Existing multi-domain RAG-QA approaches query all domains indiscriminately or rely on rigid resource selection.
Approach:	They propose a multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge.
Outcome:	The proposed framework improves multi-product knowledge-augmented QA performance on Adobe products.

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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Zirui Shao, Feiyu Gao, Zhaoqing Zhu, Chuwei Luo, Hangdi Xing, Zhi Yu, Qi Zheng, Ming Yan, Jiajun Bu

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding due to different types of annotation noise in training.
Approach:	They propose a method to reduce C&P knowledge conflicts across all tested MLLMs . they propose to use annotation noise to train models to understand document content .
Outcome:	The proposed method reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks.

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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo

Representation Learning

Challenge:	Existing approaches focus on textual data and voting records to induce political actors' stances.
Approach:	They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances.
Outcome:	The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection.

Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)

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Xiangyu Duan, Hongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

Summarization

Challenge:	Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora.
Approach:	They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task.
Outcome:	The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task.

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

Focus! Relevant and Sufficient Context Selection for News Image Captioning (2022.findings-emnlp)

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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Mingyang Zhou, Grace Luo, Anna Rohrbach, Zhou Yu

CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis (2025.acl-long)

Challenge:	Recent work only coarsely leverages the article to extract the necessary context, which makes it difficult for models to identify relevant events and named entities.
Approach:	They propose to use a vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model.
Outcome:	The proposed model significantly improves on existing models and achieves state-of-the-art on multiple benchmarks.

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Bohan Zhang, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, Jie Tang

HoWToBench: Holistic Evaluation for LLM’s Capability in Human-level Writing using Tree of Writing (2026.acl-long)

Challenge:	Existing inference scaling methods rely heavily on the quality of candidate responses . however, they are unable to produce correct answers when all candidates are flawed .
Approach:	They propose a CoT-based inference scaling strategy that leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses.
Outcome:	The proposed method improves performance on four benchmark datasets with seven policy models.

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Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan, Irene Zhou, Zikang Wang, Xiaotao Gu, Jie Tang, Hongning Wang, Minlie Huang

Challenge:	Evaluating the writing capabilities of large language models remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics.
Approach:	They propose to model the aggregation weights of sub-features in a tree-structured workflow and propose a Chinese writing benchmark that mitigates biases.
Outcome:	The proposed tree-of-writing (ToW) measures the writing capabilities of large language models (LLMs) in Chinese and shows that it mitigates biases and achieves a 0.93 Pearson correlation with human judgments.

DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)

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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks.
Approach:	They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks.
Outcome:	The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks .

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models (2026.acl-long)

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Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Jie Liu, Wenting Chen, Linlin Shen

ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning (2024.findings-acl)

Challenge:	Existing benchmarks fail to evaluate egocentric clinical intent understanding of medical multimodal large language models.
Approach:	They propose a benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation and diagnostic interpretation.
Outcome:	The proposed benchmark addresses challenges of visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols.

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Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo

Enrich, Aggregate, and Generate: Three-stage Biomedical Data-to-Text Generation Using Large Language Models in Low-resource Scenarios (2026.findings-acl)

Challenge:	Charts are an effective tool for understanding data patterns, but their combination of graphical elements and textual components poses challenges for general-purpose multimodal models.
Approach:	They propose a chart-based vision-language model for universal chart comprehension and reasoning that leverages a dataset of chart-related tasks.
Outcome:	The proposed model outperforms the state-of-the-art charts with zero-shot setting on various chart tasks.

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Yupian Lin, Guangya Yu, Yuang Bian, Cheng Yuan, Hui Luo, Tong Ruan

MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)

Challenge:	Biomedical data-to-text generation is a branch of Natural Language Generation, aiming at generating textual natural language descriptions that can fluently and precisely describe the structured data.
Approach:	They propose an LLM framework that can be used to generate textual natural language descriptions using in-context learning.
Outcome:	The proposed framework provides good interpretability and superior performance on the BioLeaflets dataset.

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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu

Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)

Challenge:	Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents .
Approach:	They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning.
Outcome:	The proposed framework outperforms existing methods in performance and inference efficiency.

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Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu

Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.acl-long)

Challenge:	Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios.
Approach:	They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character.
Outcome:	Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters.

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Yu Fu, Chen Luo, Josef Valvoda, Xin Zhang, Xuejing Lei, Xiao Pan, Hui Liu, Yue Dong

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)

Challenge:	Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored.
Approach:	They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context.
Outcome:	The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Knowledge Representation

Challenge:	Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance.
Approach:	They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results.
Outcome:	The proposed method improves on the datasets from Coursera and XuetangX.

Video-aided Unsupervised Grammar Induction (2021.naacl-main)

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Songyang Zhang, Linfeng Song, Lifeng Jin, Kun Xu, Dong Yu, Jiebo Luo

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Existing methods of multi-modal grammar induction focus on grammar inducing from text-image pairs, but videos provide even richer information, such as static objects and actions.
Approach:	They propose a video-aided grammar induction model which learns a constituency parser from unlabeled text and its corresponding video.
Outcome:	The proposed model outperforms existing systems on three benchmarks.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

Challenge:	Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding.
Approach:	They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data .
Outcome:	The proposed method improves translation quality without hurting unconstrained words.

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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension (2020.aacl-main)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

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Huaishao Luo, Yu Shi, Ming Gong, Linjun Shou, Tianrui Li

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)

Challenge:	Existing methods to predict the start and end positions of answer spans generate two probability vectors.
Approach:	They propose a method that extends the probability vector to a probability matrix.
Outcome:	The proposed method improves on SQuAD 1.1 and three other question answering benchmarks.

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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song

Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)

Challenge:	Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used.
Approach:	They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark.
Outcome:	The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history.

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Songtao Liu, Ziling Luo, Minghua Xu, Lixiao Wei, Ziyao Wei, Han Yu, Wei Xiang, Bang Wang

Challenge:	Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues.
Approach:	They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset.
Outcome:	The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets.

Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025.findings-acl)

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Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu

Cooperative Self-training of Machine Reading Comprehension (2022.naacl-main)

Challenge:	Existing methods for active retrieval (AR) rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries.
Approach:	They propose a method to identify knowledge boundaries in active retrieval by retrieving historical queries as high-confidence in-context examples.
Outcome:	Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training.

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Hongyin Luo, Shang-Wen Li, Mingye Gao, Seunghak Yu, James Glass

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

Challenge:	Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains.
Approach:	They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks.

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Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)

Challenge:	Existing LLMs are difficult to achieve satisfactory results in table-related tasks.
Approach:	They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks.
Outcome:	The proposed model achieves state-of-the-art on a Logic2Text dataset.

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Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si

CogCompNLP: Your Swiss Army Knife for NLP (L18-1)

Challenge:	Argument mining is an important research field that attracts growing attention in recent years.
Approach:	They propose a new task to extract argument pairs from peer review and rebuttal . they use an open review platform to analyze the contents, structure and connections .
Outcome:	The proposed task is based on a dataset of 4,764 fully annotated review-rebuttal passage pairs . it is able to detect argumentative propositions and extract argument pairs from the corpus .

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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth

Challenge:	a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks.
Approach:	They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community .
Outcome:	The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges.

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness .
Approach:	They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image.
Outcome:	The proposed model outperforms existing models on key downstream tasks.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

scRAG: Hybrid Retrieval-Augmented Generation for LLM-based Cross-Tissue Single-Cell Annotation (2025.findings-acl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Zhiyin Yu, Chao Zheng, Chong Chen, Xian-Sheng Hua, Xiao Luo

CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare.
Approach:	They propose a framework that integrates advanced LLM-based RAG techniques into cross-tissue single-cell annotation.
Outcome:	The proposed framework outperforms baseline models, generalist models, domain-specific methods, and trained classifiers on a cross-tissue dataset.

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Daoguang Zan, Ailun Yu, Wei Liu, Bo Shen, Shaoxin Lin, Yongshun Gong, Yafen Yao, Yan Liu, Bei Guan, Weihua Luo, Yongji Wang, Qianxiang Wang, Lizhen Cui

Challenge:	Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios.
Approach:	They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios.
Outcome:	The proposed model can train a multilingual multitasking model using less data and training data.

Factorized Transformer for Multi-Domain Neural Machine Translation (2020.findings-emnlp)

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Yongchao Deng, Hongfei Yu, Heng Yu, Xiangyu Duan, Weihua Luo