Papers by Qian Wang

UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook (2025.acl-long)

Challenge:	Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area.
Approach:	They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format.
Outcome:	The proposed model performs better on human annotators and on SOTA models with human annnotators.

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Yidi Jiang, Qian Chen, Shengpeng Ji, Yu Xi, Wen Wang, Chong Zhang, Xianghu Yue, ShiLiang Zhang, Haizhou Li

Multimodal Fusion and Coherence Modeling for Video Topic Segmentation (2025.findings-acl)

Challenge:	Existing neural audio codecs are not capable of handling multi-domain audio data . et al., 2023) integrate speech modality with text-based large language models .
Approach:	They propose a unified audio codec with a single codebook to support multi-domain audio data . they propose combining a mix-of-experts strategy and a partitioned domain-adaptive codebook method .
Outcome:	The proposed codec outperforms existing codecs on acoustic and semantic representation capabilities.

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Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu, Qian Chen, Wen Wang

Recyclable Tuning for Continual Pre-training (2023.findings-acl)

Challenge:	Traditional video topic segmentation methods struggle to discern topical transitions . supervised approaches have improved performance on video action or scene segmentation .
Approach:	They propose a new task for video topic segmentation that enhances multimodality alignment and fusion by exploring different architectures using Cross-Attention and Mixture of Experts.
Outcome:	The proposed model improves on educational videos, in the form of lectures . it combines cross-attention and mixture of experts to strengthen multimodality alignment and fusion .

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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

UniVocal: Unified Speech-Singing Code-Switching Synthesis (2026.acl-long)

Challenge:	Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded.
Approach:	They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance .
Outcome:	The proposed method improves the convergence and performance of the upgraded PLM.

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YuFei Shi, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling, Yang Ai

Speech & Audio in NLP

Challenge:	Existing systems cannot automatically determine when to switch between modes based on text content.
Approach:	They propose a unified framework that implicitly infers vocal modes from text context to pioneer SCS Synthesis.
Outcome:	The proposed framework infers vocal modes solely from text context to pioneer SCS Synthesis.

From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)

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Wang Zhang, Xin Wang, Qian Wang, Tao Deng, Xiaoru Wu

CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools (2023.emnlp-demo)

Challenge:	Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'.
Approach:	They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs.
Outcome:	The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels.

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Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold

Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)

Challenge:	a lack of transparency in sustainability reporting is a key challenge due to the sheer volume and complexity of sustainability reports . only a few entities worldwide have the resources to analyze these reports at scale . a novel LLM-based system to automate the analysis of corporate sustainability reports is needed .
Approach:	They propose a novel LLM-based system to automate the analysis of corporate sustainability reports.
Outcome:	The proposed system automates the analysis of corporate sustainability reports.

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Xinlin Zhuang, Jiahui Peng, Ren Ma, Yinfan Wang, Tianyi Bai, Xingjian Wei, Qiu Jiantao, Chi Zhang, Ying Qian, Conghui He

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling (2023.emnlp-main)

Challenge:	composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies.
Approach:	They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings.
Outcome:	The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%.

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Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu, Qian Chen, Wen Wang

Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models (2026.acl-long)

Challenge:	Recent supervised neural models have greatly promoted the development of topic segmentation, but the deeper relationship between coherence and topic segmenting is underexplored.
Approach:	They propose to use topic-aware Sentence Structure Prediction and Contrastive Semantic Similarity Learning to capture coherence from logical structure and semantic similarity perspectives to further improve topic segmentation performance.
Outcome:	The proposed approach outperforms state-of-the-art methods on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection.

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Yifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen, Wen Wang, Ziqing Wang, Yangzhuo Li, Tianle Liang, Zhou Zhao

EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbot (2024.acl-demos)

Challenge:	Reinforcement learning (RL) has improved text- and vision-language models, but its application in SDMs is hindered.
Approach:	They propose a dual-axis Generative Reward Model that provides semantic quality and interaction timing for SDMs.
Outcome:	The proposed model achieves state-of-the-art performance on interaction-quality assessment across a wide spectrum of datasets.

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Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2024.acl-long)

Challenge:	EmpathyEar is an open-source, avatar-based multimodal empathetic chatbot . currently, ERG systems rely on text, sound, and vision .
Approach:	They propose an open-source, avatar-based multimodal empathetic chatbot to fill the gap in traditional text-only ERG systems.
Outcome:	The proposed system enables users to generate emotional responses to user queries . it can also generate avatars with talking faces and synchronized speeches .

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Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng

From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation (2026.acl-long)

Challenge:	Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks .
Approach:	They propose an execution-based, multilingual, multitask evaluation benchmark for LLMs . they evaluate coding performance from three dimensions: length, difficulty, efficiency .
Outcome:	The proposed benchmark covers 43 programming languages and eight coding tasks.

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Zezhou Wang, Ziyun Zhang, Xiaoyi Zhang, Zhuzhong Qian, Yan Lu

Challenge:	Vision-language models are increasingly deployed as computer-use agents that operate desktops and browsers.
Approach:	They propose a method that turns static expert traces into policy-aligned guidance . they propose RLVR with a per-task, dynamically updated cache to decompose planning and execution .
Outcome:	The proposed model improves UITARS1.5-7B success from 22.87% to 32.13% on OSWorld-Verified and raises a held-out split from 5.74% to 10.30% on MMBench-GUI and Online-Mind2Web.

BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain (2024.findings-emnlp)

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Kaisi Guan, Qian Cao, Yuchong Sun, Xiting Wang, Ruihua Song

Information Retrieval

Challenge:	Existing work adopts separate modules for retrieval and generation, which may be suboptimal since the retrieval task and generation task cannot benefit from each other to improve performance.
Approach:	They propose a backbone-shared RAG framework that uses a domain-specific corpus to continuously pre-train a model and then trains two plug-and-play Low-Rank Adaptation modules based on the shared backbone to minimize retrieval and generation losses respectively.
Outcome:	The proposed framework outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation.

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization (2025.findings-emnlp)

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Zhibo Xu, Zhu JianHao, Jingwen Xu, Changze Lv, Zhenghua Wang, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang

Learning When to Translate for Streaming Speech (2022.acl-long)

Challenge:	federated learning approaches are limited by the complexity of large language models and the need for specialized expertise to protect intellectual property.
Approach:	They propose a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters.
Outcome:	The proposed approach maintains strong model performance in federated learning settings and achieves enhanced protection of model parameters compared to baseline methods.

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Qian Dong, Yaoming Zhu, Mingxuan Wang, Lei Li

Multilinguality

Challenge:	Existing methods waiting-and-translating for a fixed duration break speech acoustic units . Existing models waiting-for a set duration and generating partial sentences are not effective .
Approach:	They propose a monotonic segmentation module inside an encoder-decoder model to detect proper speech unit boundaries for a streaming speech input.
Outcome:	The proposed method outperforms existing methods on a speech translation dataset and achieves the best trade-off between translation quality and latency.

SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)

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Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, Suge Wang

Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (2023.emnlp-main)

Challenge:	Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability.
Approach:	They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation.
Outcome:	The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods .

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Xue Han, Yitong Wang, Qian Hu, Pengwei Hu, Chao Deng, Junlan Feng

Named Entity Recognition

Challenge:	Existing name entity recognition methods combine pre-trained language models with supervised models such as BiLSTM/LSTM-CRF to perform poorly in a spoken dialogue context.
Approach:	They propose a logic-guided fine-grained address recognition method that softly applies the logic rule to improve the accuracy of FGAER.
Outcome:	The proposed method improves fine-grained address entity recognition from multi-turn spoken dialogues.

UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework (2025.coling-demos)

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Yike Zhao, Xiaoman Wang, Yunshi Lan, Weining Qian

Syntactic Text Processing

Challenge:	Existing tools for GEC have been developed to support research on grammatical errors, but there is no comprehensive evaluation on these models.
Approach:	They propose an open-source framework for Grammatical Error Correction that integrates 5 widely-used GEC models and compares their performance on 7 datasets in different languages.
Outcome:	The proposed framework compares 5 widely-used models on 7 datasets in different languages.

MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs (2025.findings-acl)

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Qian Wang, Tianyu Wang, Zhenheng Tang, Qinbin Li, Nuo Chen, Jingsheng Liang, Bingsheng He

Green & Sustainable NLP

Challenge:	Existing multi-agent systems lack agent coordination and rely on predefined procedures . existing systems lack adaptive task coordination when task is big and complex .
Approach:	They propose a large-scale autonomous LLM-based multi-agent system that generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication and comprehensive system monitoring.
Outcome:	The proposed system outperforms existing systems in task completion efficiency and scalability.

MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)

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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

Challenge:	Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets.
Approach:	They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity.
Outcome:	Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets.

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Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, ShiLiang Zhang, Bing Li, Wei Wang, Xin Cao

SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)

Challenge:	Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach:	They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome:	The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .

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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma

Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)

Challenge:	Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments.
Approach:	They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods.
Outcome:	Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG.

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Yuemin Chen, Feifan Wu, Jingwei Wang, Hao Qian, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning (2022.findings-emnlp)

Challenge:	Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts.
Approach:	They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph.
Outcome:	The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

Generative Table Pre-training Empowers Models for Tabular Prediction (2023.emnlp-main)

Challenge:	Existing approaches to learning KG triplets ignore ternary propagation patterns and ignore zero-shot, few-shot and synonymity problems.
Approach:	They propose a framework for contrastive learning based on ternary propagation patterns among head, relation and tail.
Outcome:	Experiments on benchmarks show that TernaryCL is superior to state-of-the-art models.

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Tianping Zhang, Shaowen Wang, Shuicheng Yan, Li Jian, Qian Liu

LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation (2025.naacl-long)

Challenge:	Existing methods to use table pre-training to boost tabular prediction performance remain open . a bachelor's degree earns less than 50K, and a generative LM can be used to unify tasks via one LM.
Approach:	They propose a method that leverages table pre-training to empower tabular prediction models.
Outcome:	The proposed method outperforms baseline models on 12 datasets and can be easily combined with various backbone models.

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Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras

Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering (2022.coling-1)

Challenge:	Recent code completion models focus on local file contexts, but do not fully capture the complexities of real-world software development.
Approach:	They propose a version-specific code-completion task across eight libraries as they evolve over the years and an in-depth analysis of two widely used public libraries: PyTorch and Matplotlib.
Outcome:	The proposed model improves performance with public libraries, compared with existing models.

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Qian Wang, Jiajun Zhang

Multilinguality

Challenge:	Existing clustering methods cannot handle asymmetric problem in multilingual NMT . existing models cannot handle the asymmetry problem since there are thousands of languages involved .
Approach:	They propose a fuzzy task clustering method to address the asymmetric problem in multilingual NMT by using task affinity as the clustering criterion.
Outcome:	The proposed method outperforms baselines for a multilingual model and the existing models.

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

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

Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment (2023.findings-emnlp)

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.

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Qian Li, Cheng Ji, Shu Guo, Zhaoji Liang, Lihong Wang, Jianxin Li

NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)

Challenge:	Multi-modal entity alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs).
Approach:	They propose a novel MMEA transformer that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance alignment task.
Outcome:	The proposed transformer hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.

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Wei Liu, Siya Qi, Xinyu Wang, Chen Qian, Yali Du, Yulan He

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

Challenge:	Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available.
Approach:	They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier.
Outcome:	The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%.

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

Green & Sustainable NLP

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

From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)

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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji

SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

Challenge:	Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time.
Approach:	They propose a framework that reframes language modeling as next-state prediction under interaction.
Outcome:	The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics .

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Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang

TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025.findings-acl)

Challenge:	Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach:	They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome:	The proposed model outperforms existing models in symbolic song composition tasks.

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Yuheng Lu, Qian Yu, Hongru Wang, Zeming Liu, Wei Su, Yanping Liu, Yuhang Guo, Maocheng Liang, Yunhong Wang, Haifeng Wang

Challenge:	Existing GUI agents struggle to adapt to dynamic and interconnected nature of real-world digital environments, authors show .
Approach:	They propose a benchmark to evaluate the transferability of GUI agents across three key dimensions . transBench includes 15 app categories with diverse functionalities .
Outcome:	The proposed benchmark shows that existing GUI agents struggle to adapt to dynamic, interconnected environments.

StreamingEval: A Unified Evaluation Framework towards Realistic Streaming Video Understanding (2026.findings-acl)

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Tang Guowei, Tianwen Qian, Huanran Zheng, Wang Yifei, Xiaoling Wang

Learning to Decipher Hate Symbols (N19-1)

Challenge:	Existing research on streaming video understanding focuses on isolated aspects of visual understanding, but ignores practical deployability under realistic resource constraints.
Approach:	They propose a framework to evaluate streaming video understanding capabilities under realistic constraints.
Outcome:	StreamingEval benchmarks offline and online video models under a standardized protocol . it evaluates visual encoding efficiency, text decoding latency and task performance .

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Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024.acl-long)

Challenge:	Existing computational models of hate speech focus on a binary or multiclass classification task . a recent study shows an alarming 4.6% increase in hate speech in 2016 .
Approach:	They propose a task of deciphering hate symbols using the Urban Dictionary . they propose ciphers using Sequence-to-Sequence models and a Variational Decipher .
Outcome:	The proposed model can crack hate symbols based on context and generalize better to unseen symbols in a more challenging testing setting.

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Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding (2023.findings-acl)

Challenge:	Experimental results show that fine-tuning of large language models for specific tasks can be challenging . distribution shift during fine-timing can lead to performance degradation in general task capabilities .
Approach:	They propose a new approach that bridges the distribution gap between task datasets and LLMs by guiding fine-tuning with a distilled dataset generated by the model itself.
Outcome:	The proposed approach achieves comparable or superior performance on downstream tasks compared to the vanilla approach.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang, Chengwei Qin

Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction (2026.findings-acl)

Challenge:	Existing methods for inductive knowledge Graphs are limited by sparsity and implicit transfer.
Approach:	They propose a Contrastive Learning framework with graph guided Variational autoencoder on Meta-KGs to capture and transfer entities.
Outcome:	The proposed framework outperforms state-of-the-art methods with extensive experiments.

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Fanghao Lou, Qiqi Wang, Guanyu Chen, Senbo Zhang, Kaiqi Zhao, Qian Liu, Huijia Li

FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026.acl-long)

Challenge:	Recent advances leverage large language models (LLMs) for legal reasoning, but they face high computational costs and information degradation when handling long cases.
Approach:	They propose a framework that selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning.
Outcome:	The proposed framework outperforms existing methods on four real-world datasets spanning multiple jurisdictions and languages.

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Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov

Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)

Challenge:	Existing benchmarks emphasize final numerical answers while neglecting intermediate reasoning steps.
Approach:	They propose a symbolic benchmark for verifiable Chain-of-Thought evaluation in finance . FINCHAIN spans 58 topics across 12 financial domains and three difficulty levels .
Outcome:	The proposed benchmark aims to bridge symbolic reasoning and factual verification.

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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang

Pub-LawBench: Public-Oriented Benchmarking for LegalAI (2026.acl-long)

Challenge:	Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets.
Approach:	They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains.
Outcome:	The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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Qiaoyu Zheng, Zehan Ma, Yijing Zhang, Qiqi Wang, Huijia Li, Qian Liu

Challenge:	Existing evaluation frameworks focus on legal professionals, not legal professionals.
Approach:	They propose a public-oriented LegalAI benchmark grounded in legal functionalism and genre analysis to address this gap.
Outcome:	The proposed model evaluates 17 large language models on Pub-LawBench using simple prompts and Chain-of-Thought under a vanilla inference setting.

CoCoID: Learning Contrastive Representations and Compact Clusters for Semi-Supervised Intent Discovery (2022.emnlp-industry)

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Qian Cao, Deyi Xiong, Qinlong Wang, Xia Peng

Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)

Challenge:	Existing approaches to intent discovery cluster novel intents with prior knowledge from intent-labeled data in a semi-supervised way.
Approach:	They propose a semi-supervised intent discovery framework CoCoID with two components . they propose to discriminate user utterance representation learning and intra-cluster knowledge distillation .
Outcome:	The proposed framework outperforms state-of-the-art intent discovery models by over 1.4 ACC and ARI points and 1.1 NMI points across four datasets.

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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han

Challenge:	Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents.
Approach:	They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation.
Outcome:	Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks.

Limitations of Language Models in Arithmetic and Symbolic Induction (2023.acl-long)

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Jing Qian, Hong Wang, Zekun Li, Shiyang Li, Xifeng Yan

Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs (2026.acl-long)

Challenge:	Recent work has shown that large pretrained Language Models (LMs) can perform remarkably well on a range of NLP tasks but they have limitations on basic symbolic manipulation tasks such as copy, reverse, and addition.
Approach:	They propose to use explicit positional markers, fine-grained computation steps, and LMs with callable programs to teach large pretrained Language Models.
Outcome:	The proposed model can perform 100% accuracy in OOD and repeating symbols.

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Zifeng Cheng, Lingyun Qian, Zhiwei Jiang, Cong Wang, Yafeng Yin, Fei Shen, Ao Zhou, Qing Gu

Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)

Challenge:	Existing methods for extracting conditional text embeddings from large language models (LLMs) relying on prompts often fails to produce high-quality conditional embeddables, resulting in degradation of quality.
Approach:	They propose a plug-and-play method that constructs unconditional general text embeddings and uses them to refine conditional text embeds.
Outcome:	The proposed method improves performance of prompt-based methods on clustering, Semantic Textual Similarity, and triplet alignment datasets.

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Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang

Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models (2026.acl-long)

Challenge:	Existing bias mitigation techniques have a negative effect on NRE, a study finds .
Approach:	They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE .
Outcome:	The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set.

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Zirui Song, Qian Jiang, Mingxuan Cui, Mingzhe Li, Lang Gao, Zeyu Zhang, Zixiang Xu, Yanbo Wang, Guangxian Ouyang, Zhenhao Chen, Xiuying Chen

ProgressLM: Towards Progress Reasoning in Vision-Language Models (2026.acl-long)

Challenge:	a recent study evaluated large audio-language models against jailbreak attacks . a new benchmark is being developed to evaluate LAM safety against jailbreaking attacks based on temporal and semantic nature of speech .
Approach:	They propose a benchmark to evaluate LAM jailbreak vulnerabilities in adversarial audio prompts . they use a dataset of 1,495 adversarials to evaluate their performance .
Outcome:	The proposed benchmark evaluates state-of-the-art LAMs against jailbreak attacks . it demonstrates that even small, semantically preserved perturbations can reduce safety .

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Jianshu Zhang, Chengxuan Qian, Haosen Sun, Haoran Lu, Dingcheng Wang, Letian Xue, Han Liu

RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)

Challenge:	Existing models for task progress estimation lack long-horizon and dynamic reasoning . estimating how much of a task has been completed requires long-term reasoning based on partial information.
Approach:	They propose a benchmark for evaluating progress reasoning from a single observation . they instantiate a two-stage paradigm that combines episodic retrieval with mental simulation .
Outcome:	The proposed benchmark improves on 14 VLMs on a small scale and shows common failure patterns.

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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai

XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration (2026.acl-long)

Challenge:	RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images .
Approach:	They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a .
Outcome:	The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images.

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Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He

Challenge:	Existing systems are designed for general-purpose scientific text generation and fail to support high-quality scientific writing beyond surface-level polishing.
Approach:	They propose a human-AI collaboration framework for academic paper revision based on criteria-guided intent alignment and context-aware modeling.
Outcome:	The proposed framework outperforms existing LLMs and rivals the quality of proprietary ones.

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

Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)

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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Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, Zhifei Li

AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Existing methods to detect identity fraud are prone to errors and are not based on real data.
Approach:	They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information.
Outcome:	The proposed system can detect fraudsters and achieve higher recognition accuracy compared with rule-based systems.

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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian

Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)

Challenge:	Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions.
Approach:	They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements.
Outcome:	Experiments on two temporal QA benchmarks show the proposed method works.

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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang

Information Retrieval

Challenge:	Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains.
Approach:	They propose several strategies for deploying RAG that balance performance and efficiency.
Outcome:	The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy.

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

X-ACE: Explainable and Multi-factor Audio Captioning Evaluation (2024.findings-acl)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Qian Wang, Jia-Chen Gu, Zhen-Hua Ling

Incremental Learning from Scratch for Task-Oriented Dialogue Systems (P19-1)

Challenge:	Existing evaluation metrics for automated audio captioning only provide an overall score . current evaluation checklists are inadequate to characterize the nuanced differences .
Approach:	They propose an explainable and multi-factor audio captioning evaluation paradigm . they define sound event, source, attribute and relation as four factors tailored for the audio description .
Outcome:	The proposed evaluation paradigm improves the quality of audio captions . it can detect mismatches and align with human perception, the authors show .

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Weikang Wang, Jiajun Zhang, Qian Li, Mei-Yuh Hwang, Chengqing Zong, Zhifei Li

BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents (2024.acl-long)

Challenge:	Existing task-oriented dialogue systems cannot guarantee that all user needs are taken into account in the design phase.
Approach:	They propose a new incremental learning framework to design task-oriented dialogue systems without pre-defining user needs.
Outcome:	The proposed framework is robust to unconsidered user actions and can update itself online with less annotation cost.

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Yifei Wang, Dizhan Xue, Shengjie Zhang, Shengsheng Qian

Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (2023.findings-acl)

Challenge:	Xi et al., 2023) show that LLM-based intelligent agents are vulnerable to backdoor attacks.
Approach:	They propose to build LLM-based intelligent agents using trained LLMs and fine-tune them on data for the agent task.
Outcome:	The proposed methods are robust even after fine-tuning on trusted data.

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Qian Li, Shu Guo, Cheng Ji, Xutan Peng, Shiyao Cui, Jianxin Li, Lihong Wang

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)

Challenge:	Existing methods for multi-modal relation extraction lack useful visual information.
Approach:	They propose a novel multi-modal relation extraction framework to capture deeper correlations of text, entity pair, and image/objects.
Outcome:	The proposed framework captures the deeper correlations of text, entity pair, and image/objects, and extracts useful information.

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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020.aacl-main)

Challenge:	DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say .
Approach:	They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset .
Outcome:	a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data .

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Qian Wang, Jiajun Zhang, Lemao Liu, Guoping Huang, Chengqing Zong

Machine Translation

Challenge:	Existing methods for machine translation require intensive keyboard interaction, which is inconvenient on mobile devices.
Approach:	They propose a touch-based editing method that is more flexible than keyboard-mouse-based translation postediting.
Outcome:	The proposed method significantly outperforms existing interactive translation methods on translation datasets and on post-editing datasets.

Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control (2024.eacl-long)

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Yunzhe Li, Qian Chen, Weixiang Yan, Wen Wang, Qinglin Zhang, Hari Sundaram

Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)

Challenge:	Existing studies on outline-conditioned text generation focus on generating text using provided outlines as rough sketches, but lack of clarity and rationality of the rough outlines hampers quality of the generated text.
Approach:	They propose a novel task that requires generating stories based on specific, sentence-level outlines.
Outcome:	The proposed framework improves the quality of precise outline-conditioned text generation.

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Jingyi Wang, Jiaqi Huang, Zunnan Xu, Jun Zhou, Kehong Yuan, Xiang Qian

Syntactic Text Processing

Challenge:	Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax.
Approach:	They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification.
Outcome:	The proposed framework significantly accelerates inference without additional training.

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy (2024.emnlp-main)

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YongKang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schuetze

A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)

Challenge:	Existing approaches to fine-tuning language models use zeroth-order optimizers to conserve GPU memory.
Approach:	They propose a full-parameter fine-tuning strategy which updates a subset of parameters at each training step.
Outcome:	The proposed approach reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage.

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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu

PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes (2026.findings-acl)

Challenge:	Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters.
Approach:	They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases.
Outcome:	The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters .

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Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	Existing home assistants struggle to interpret elliptical commands based on ellipine expressions . current assistants overlook the progressive omission that occurs in human dialogue as context accumulates - limiting their effectiveness in real-world applications .
Approach:	They propose a simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes.
Outcome:	The proposed dataset shows that existing home assistants struggle to execute user-intended operations based solely on elliptical commands.

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

Speculating LLMs’ Chinese Training Data Pollution from Their Tokens (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Qingjie Zhang, Di Wang, Haoting Qian, Liu Yan, Tianwei Zhang, Ke Xu, Qi Li, Minlie Huang, Hewu Li, Han Qiu

Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework (2025.acl-long)

Challenge:	Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT’s vocabulary behaves the worst: more than 23% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling.
Approach:	They propose to locate Polluted Chinese (PoC) tokens in LLMs and build a PoC token detector to label them in vocabularies by considering each token’s semantics and related contents from the search engines.
Outcome:	The proposed method predicts that the ratio of “*” related webpages in GPT-4o's training data is around 0.5%.

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Jundong Xu, Hao Fei, Meng Luo, Qian Liu, Liangming Pan, William Yang Wang, Preslav Nakov, Mong-Li Lee, Wynne Hsu

Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models (2021.emnlp-main)

Challenge:	Existing systems fail to fully leverage the structure of logical tasks throughout the reasoning process, causing bottlenecks in efficiency and efficacy.
Approach:	They propose a logic-complete reasoning framework, Aristotle, which integrates symbolic expressions and logical rules into the entire reasoning process.
Outcome:	The proposed framework outperforms state-of-the-art reasoning frameworks in accuracy and efficiency.

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Yiwen Wang, Jennifer Hu, Roger Levy, Peng Qian

Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)

Challenge:	Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement.
Approach:	They train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on Mandarin Chinese datasets.
Outcome:	The proposed models learn aspects of Mandarin Chinese grammar that assess syntactic and semantic relationships.

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Yang Wu, Haoze Wang, Qian Li, Jun Zhang, Huan Yu, Jie Jiang

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search (2026.acl-long)

Challenge:	Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent.
Approach:	They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system.
Outcome:	The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency.

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Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao

MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts (2025.findings-emnlp)

Challenge:	Existing methods for memory management struggle to capture fine-grained semantic relations between queries and documents.
Approach:	They propose a framework for reasoning and agentic search that grows fine-grained memory fragments from seed tokens from queries, then retraces and deep refines the memory via a contribution function.
Outcome:	Experiments on eight benchmark datasets show that MemSearch-o1 significantly mitigates memory dilution and more effectively activates reasoning potential of diverse LLMs.

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Qing Wang, Xue Han, Jiahui Wang, Lehao Xing, Qian Hu, Lianlian Zhang, Chao Deng, Junlan Feng

When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)

Challenge:	MultiPL is a special case of multiple natural languages and requires limited computational resources to generate multilingual code.
Approach:	They propose to extend LLMs by combining two paired experts to optimize expert selection at token and segment levels.
Outcome:	The proposed extension improves the performance of the base LLMs while retaining the most popular ones using limited computational resources.

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Jingwei Ni, Zhijing Jin, Qian Wang, Mrinmaya Sachan, Markus Leippold

SMART: Self-Aware Agent for Tool Overuse Mitigation (2025.findings-acl)

Challenge:	Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them.
Approach:	They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets.
Outcome:	The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it.

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Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, Heng Ji

AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)

Challenge:	Current Large Language Models (LLMs) lack self-awareness to balance reasoning and tool use, increasing computational overhead.
Approach:	They propose a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse.
Outcome:	The proposed model reduces tool use by 24% while improving performance by over 37%.

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Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin

Information Retrieval

Challenge:	Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms.
Approach:	They propose a framework that leverages coordinated agents to manage memory across multiple granularities.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%.

How to Build User Simulators to Train RL-based Dialog Systems (D19-1)

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Weiyan Shi, Kun Qian, Xuewei Wang, Zhou Yu

MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension (2023.findings-emnlp)

Challenge:	RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive.
Approach:	They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators.
Outcome:	The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly.

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Guoxin Chen, Yiming Qian, Bowen Wang, Liangzhi Li

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)

Challenge:	Existing soft prompt methods focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset.
Approach:	They propose a multi-level prompt tuning method that utilizes prompts at task-specific, domain-specific and context-specific levels to enhance the comprehension of input semantics.
Outcome:	The proposed method improves on 12 benchmarks on various QA formats and achieves an average improvement of 1.94% over the state-of-the-art methods.

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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)

Challenge:	Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling.
Approach:	They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation.
Outcome:	The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes.

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Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin

Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (2024.naacl-long)

Challenge:	Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time.
Approach:	They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.
Outcome:	The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks.

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Lihua Qian, Mingxuan Wang, Yang Liu, Hao Zhou

OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)

Challenge:	Experimental results show that non-autoregressive generation models are superior in generation efficiency but inferior in generation quality.
Approach:	They propose a diffusion glancing transformer which employs a modality diffusion process and residual glancy sampling to improve multi-modality modeling.
Outcome:	The proposed model outperforms autoregressive and non-autoregressive models on machine translation and text generation benchmarks.

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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang

SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)

Challenge:	Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge .
Approach:	They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM.
Outcome:	The proposed model can model human conversation behaviors with low latency and natural interactions with low delay.

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Wenda Xu, Xian Qian, Mingxuan Wang, Lei Li, William Yang Wang

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)

Challenge:	Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks.
Approach:	They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus.
Outcome:	The proposed model outperforms all prior unsupervised metrics on four text generation evaluation benchmarks, with an average Kendall improvement of 0.158.

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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu

Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)

Challenge:	Large language models (LLMs) have shown remarkable achievements across various language tasks.
Approach:	They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training .
Outcome:	The proposed system provides literature investigation, paper reading, and academic writing functions.

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Ruonan Wang, Yuxi Qian, Fangxiang Feng, Xiaojie Wang, Huixing Jiang

Visual Data in NLP

Challenge:	Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions.
Approach:	They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one.
Outcome:	The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets.

MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)

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Feiyan Liu, Liangzhi Li, Xiaoli Wang, Feng Luo, Chang Liu, Jinsong Su, Yiming Qian

LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection (2025.findings-acl)

Challenge:	Effective EHR representations are key to achieving high performance in healthcare applications.
Approach:	They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes.
Outcome:	The proposed model outperforms baseline models on two real clinical datasets in downstream tasks.

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Tianyi Ma, Yiyue Qian, Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling (2026.eacl-long)

Challenge:	Existing approaches to combat illicit drug trafficking are impractical due to the scarcity of labeled samples and imbalance of classes.
Approach:	They propose a Large Language Model-empowered Heterogeneous Graph Prompt Learning framework for illicit drug trafficking detection that leverages LLM to facilitate heterogeneous graph neural networks to effectively identify minority classes.
Outcome:	The proposed framework is able to identify minority classes in class-imbalanced scenarios.

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Qian Wang, Ziqi Huang, Ruoxi Jia, Paul Debevec, Ning Yu

A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)

Challenge:	Experimental results show that MAViS achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness.
Approach:	They propose an end-to-end multi-agent collaborative framework for long-sequence video storytelling that orchestrates specialized agents across multiple stages.
Outcome:	The proposed framework achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness.

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Ray Oshikawa, Jing Qian, William Yang Wang

Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning (2026.acl-long)

Challenge:	Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety .
Approach:	They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks.
Outcome:	The proposed models are more fine-grained, detailed, fair, and practical.

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Yitong Wang, Xue Han, WenChun Gao, Qian Hu, Jiahui Wang, Ziqing Wang, Lijun Mei, Junlan Feng

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)

Challenge:	Existing methods for continual learning (CL) are designed to mitigate catastrophic forgetting while neglecting knowledge sharing across tasks.
Approach:	They propose a framework that facilitates knowledge transfer while mitigating catastrophic forgetting by assigning task-specific parameter subspaces to new tasks . they then leverage attribution scores to evaluate task similarity and employ soft orthogonality between task- specific subspace .
Outcome:	The proposed framework facilitates knowledge transfer while mitigating catastrophic forgetting.

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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian

Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)

Challenge:	Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics.
Approach:	They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities.
Outcome:	The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly.

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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang

Audio-Aware Large Language Models as Judges for Speaking Styles (2025.findings-emnlp)

Challenge:	Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments.
Approach:	They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization.
Outcome:	The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings .

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Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang

QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models (2025.findings-naacl)

Challenge:	Audio-aware large language models (ALLMs) can understand textual and non-textual information in the audio input.
Approach:	They use audio-aware large language models (ALLMs) to evaluate the speaking styles of SLMs on two tasks: voice style instruction following and role-playing.
Outcome:	The proposed models can understand the textual and non-textual information in the audio input and can be used as a judge to assess the speaking styles of SLMs.

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Changhai Zhou, Yuhua Zhou, Yibin Wang, Shijie Han, Qian Qiao, Hongguang Li

Understanding the Dark Side of LLMs’ Intrinsic Self-Correction (2025.acl-long)

Challenge:	Structured pruning can reduce model size but results in significant accuracy degradation . quantization and pruning increase the difficulty of fine-tuning, requiring a more refined quantization scheme.
Approach:	They propose a structured pruning framework followed by a layer-wise mixed-precision quantization scheme to reduce model memory consumption during fine-tuning and inference.
Outcome:	Experiments on benchmark datasets show that QPruner outperforms existing methods in memory savings while maintaining or improving model performance.

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Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, Han Qiu

Challenge:	Recent studies show that LLMs’ intrinsic self-correction fails without oracle labels as feedback.
Approach:	They propose to use one simple task and three complex tasks with state-of-the-art LLMs like ChatGPT, Llama, and DeepSeek to interpret LLM's intrinsic self-correction.
Outcome:	The proposed methods reveal the dark side of LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.

MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)

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Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Robert Tang, Heng Ji, Jiaxuan You

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.
Approach:	They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
Outcome:	The proposed framework measures task completion and quality of collaboration and competition using novel, milestone-based key performance indicators.

Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

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Xinnan Guo, Qian Zhu, Qiuhui Shi, Xuan Lin, Liubin Wang, DaqianLi DaqianLi, Yongrui Chen

HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)

Challenge:	Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach:	They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome:	The proposed method improves existing models and achieves state-of-the-art on three benchmarks.

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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)

Challenge:	Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions.
Approach:	They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles.
Outcome:	The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system.

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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian

Knowledge Representation

Challenge:	Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events.
Approach:	They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events.
Outcome:	The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making .

Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)

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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format.
Approach:	They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision.
Outcome:	The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu

Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)

Challenge:	Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding .
Approach:	They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction .
Outcome:	The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction .

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Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin

Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants (2022.emnlp-industry)

Challenge:	Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices.
Approach:	They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop.
Outcome:	The proposed method can perform better than current SOTA methods in compressing Transformer-based language models.

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Cheng Qian, Haode Qi, Gengyu Wang, Ladislav Kunc, Saloni Potdar

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling (2026.acl-long)

Challenge:	Out of Scope (OOS) detection is a problem with chatbots that cannot make sense of a query . a real-world solution to this problem is to identify out-of-domain queries .
Approach:	They propose a simple yet effective OOS detection method that outperforms standard methods . they propose analyzing data from an enterprise virtual assistant platform to test the method .
Outcome:	The proposed method outperforms standard OOS detection methods in a real-world deployment of virtual assistants.

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Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li, Zhen-Hua Ling

Challenge:	Large Language Models (LLMs) extend their capabilities through function-calling (FC) however, obtaining and annotating real function-called data is challenging, and synthetic data from existing pipelines suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control.
Approach:	They propose a pipeline for generating FC training data using reliable tools and a multi-agent framework that supports a dialogue generation system that produces conversations spanning diverse scenarios.
Outcome:	The proposed pipeline outperforms open-source models in in-domain FC performance and out-of-domain generalization while reaching FC capabilities comparable to some of the latest API-based models.

LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains (2026.acl-long)

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Weizhe Shi, Qiqi Wang, Yihong Pan, Qian Liu, Kaiqi Zhao

Controllable Dialogue Simulation with In-context Learning (2022.findings-emnlp)

Challenge:	Current legalAI tasks divide sentencing and legal reasoning into two separate tasks, resulting in inconsistency between the reasoning and predictions.
Approach:	They propose a new task that generates both legal reasoning and sentencing decisions using a framework that applies structured legal chains to guide the model through comprehensive case assessments.
Outcome:	The proposed model outperforms baseline models on real-world, open-source Chinese legal case datasets.

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Zekun Li, Wenhu Chen, Shiyang Li, Hong Wang, Jing Qian, Xifeng Yan

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)

Challenge:	Existing methods to generate annotated dialogues require crowdsourcing, which is expensive and time-consuming.
Approach:	They propose a dialogue simulation method based on large language model in-context learning that generates new dialogues and annotations in a controllable way.
Outcome:	The proposed method can expand a small set of dialogue data with minimum or zero human involvement and parameter update.

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Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu

FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting (2026.findings-acl)

Challenge:	XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications.
Approach:	They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy .
Outcome:	The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences.

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Fengxian Ji, Jingpu Yang, Zirui Song, Yuanxi Wang, Zhexuan Cui, Yuke Li, Qian Jiang, Xiuying Chen

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

Challenge:	FineState-Bench evaluates whether an agent can correctly ground an instruction to the intended UI control and reach the exact target state.
Approach:	They propose a benchmark that evaluates whether an agent can correctly ground an instruction to the intended UI control and reach the exact target state.
Outcome:	The proposed benchmark evaluates whether an agent can ground an instruction to the intended UI control and reach the exact target state.

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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.

Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)

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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang

Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable (2026.findings-acl)

Challenge:	Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning.
Approach:	They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings.
Outcome:	Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks.

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Zhenheng Tang, Qihua Pan, Jingya Shen, Xiang Liu, Qian Wang, Bo Li, Xiaowen Chu

DecisionFlow: Advancing Large Language Model as Principled Decision Maker (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have similar value rankings but little is known about how susceptible they are to external influence and how different values are correlated with each other.
Approach:	They propose to use 6 different value transformation prompting methods to examine the plasticity of LLM value systems by comparing them with 8 LLMs.
Outcome:	The proposed methods are effective on 8 LLMs and 3 families.

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Xiusi Chen, Shanyong Wang, Cheng Qian, Hongru Wang, Peixuan Han, Heng Ji

Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)

Challenge:	Current language models lack the structured deliberation needed for high-stakes tasks such as healthcare and finance.
Approach:	They propose a decision-making framework that guides models to reason over structured representations of actions, attributes, and constraints.
Outcome:	The proposed framework achieves up to 30% accuracy gains over strong prompting baselines and enhances alignment in outcomes.

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Yuanyuan Liang, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, Yunshi Lan

CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)

Challenge:	Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks.
Approach:	They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms .
Outcome:	The proposed method outperforms baselines on three public KBQG datasets.

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Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras

The Ranking Blind Spot: Decision Hijacking in LLM-based Text Ranking (2025.emnlp-main)

Challenge:	Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations .
Approach:	They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies.
Outcome:	The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations.

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Yaoyao Qian, Yifan Zeng, Yuchao Jiang, Chelsi Jain, Huazheng Wang

Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated strong performance in information retrieval tasks like passage ranking.
Approach:	They propose two attacks that aim to force the LLM ranker to prefer a specific passage and rank it at the top.
Outcome:	The proposed attacks aim to force the LLM ranker to prefer a specific passage and rank it at the top.

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Nuo Chen, Yicheng Tong, Yuzhe Yang, Yufei He, Xueyi Zhang, Zou Qingyun, Qian Wang, Bingsheng He

Challenge:	Multi-agent systems (MAS) are increasingly used for open-ended idea generation . when and why collective interaction expands the solution space remains unclear .
Approach:	They propose to study diversity in multi-agent systems across three bottom-up levels: model intelligence, agent cognition, and system dynamics.
Outcome:	The proposed model yields diminishing diversity despite higher quality . the proposed model fails to expand diversity and causes it to collapse .

Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)

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Chenyu Qiu, Pengjiang Qian, Chuang Wang, Jian Yao, Li Liu, Fang Wei, Eddie Eddie

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)

Challenge:	Existing knowledge graph completion models require longer training and inference times as well as increased memory usage.
Approach:	They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations.
Outcome:	The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets.

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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun

MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)

Challenge:	Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations.
Approach:	They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples.
Outcome:	The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions.

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Yiheng Hu, Xiaoyang Wang, Qing Liu, Xiwei Xu, Qian Fu, Wenjie Zhang, Liming Zhu

ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges (2025.findings-emnlp)

Challenge:	Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps.
Approach:	They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types .
Outcome:	The proposed framework outperforms existing models that rely on training.

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Cheng Qian, Hongyi Du, Hongru Wang, Xiusi Chen, Yuji Zhang, Avirup Sil, ChengXiang Zhai, Kathleen McKeown, Heng Ji

Numerical Reasoning

Challenge:	Existing benchmarks for large language models fail to reflect real-world complexity . existing benchmarks often fail to capture real-life problems .
Approach:	They propose a benchmark that features real-world-inspired, open-ended problems from competitions . they propose 'ModelingBench' that supports multiple valid solutions .
Outcome:	The proposed framework outperforms baselines and produces well-grounded, creative solutions.

ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)

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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng, Zhou Zhao

Experiential Co-Learning of Software-Developing Agents (2024.acl-long)

Challenge:	Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation.
Approach:	They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space.
Outcome:	The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions.

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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)

Challenge:	Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents.
Approach:	They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution.
Outcome:	The proposed framework enables agents to tackle unseen software-developing tasks more effectively.

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Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Representation Learning

Challenge:	Existing methods that focus on a single tweet as input are likely to yield high false positive and negative rates.
Approach:	They propose a model that leverages intra-user and inter-user representation learning to improve hate speech detection on Twitter by suppressing the noise in a single Tweet.
Outcome:	The proposed model significantly improves the f-score of a strong bidirectional LSTM model by 10.1%.

AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)

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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo

Challenge:	Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking.
Approach:	They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation.
Outcome:	The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts.

DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)

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Senbo Zhang, Qiqi Wang, Fanghao Lou, Guanyu Chen, Yihong Pan, Huijia Li, Qian Liu

Challenge:	Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning.
Approach:	They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims.
Outcome:	The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches.

Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang

NCLS: Neural Cross-Lingual Summarization (D19-1)

Challenge:	Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach:	They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome:	The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.

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Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Chengqing Zong

Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)

Challenge:	Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation.
Approach:	They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization.
Outcome:	The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets.

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Yuheng Lu, Bingshuo Qian, Caixia Yuan, Huixing Jiang, Xiaojie Wang

SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)

Challenge:	Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods.
Approach:	They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space.
Outcome:	The proposed method reduces scale of output change while introducing minimal constraint on model capacity.

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Hongfei Xia, Hongru Wang, Zeming Liu, Qian Yu, Yuhang Guo, Haifeng Wang

Hierarchical CVAE for Fine-Grained Hate Speech Classification (D18-1)

Challenge:	Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking.
Approach:	They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets.
Outcome:	The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.

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Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Text Classification

Challenge:	Existing work on automated hate speech detection focuses on binary classification or on differentiating among a small set of categories.
Approach:	They propose a method to discriminate among 40 hate groups of 13 different hate group categories.
Outcome:	The proposed method outperforms discriminative models on a fine-grained hate speech classification task.

Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)

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Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li

Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models (2025.findings-emnlp)

Challenge:	Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.
Approach:	They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup.
Outcome:	The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer.

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Zezhou Wang, Yaxin Du, Xingjun Ma, Yu-Gang Jiang, Zhuzhong Qian, Siheng Chen

TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction (2023.findings-emnlp)

Challenge:	Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data.
Approach:	They introduce an algorithm that explicitly maximizes cross-client domain coverage through diversity-oriented client center selection and retrieval-based augmentation.
Outcome:	The proposed algorithm achieves performance gains of 29.19% and domain coverage improvements of 4.82%-21.36% over 11 baselines.

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Junyi Liu, Liangzhi Li, Tong Xiang, Bowen Wang, Yiming Qian

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Challenge:	ChatGPT and GPT-4 are commercial large language models (LLMs) however, they may produce vague responses or incorrect answers in certain specialized domains.
Approach:	They propose a token compression scheme that uses summarization and semantic compression to reduce the token size of LLMs.
Outcome:	The proposed method reduces token size by doing summarization and semantic compression while reducing token size with only 1.6% accuracy drop.

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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian

RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)

Challenge:	Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases.
Approach:	They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction.
Outcome:	The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database.

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Fei Zhao, Chonggang Lu, null Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, Shaosheng Cao

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement.
Approach:	They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services.
Outcome:	The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models.

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Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning (2026.findings-acl)

Challenge:	Recent advances in text generation have limited applications due to multimodality problem.
Approach:	They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem.
Outcome:	The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

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Zhiyuan Chang, Mingyang Li, Yuekai Huang, Ziyou Jiang, Xiaojun Jia, Qian Xiong, Junjie Wang, Zhaoyang Li, Qing Wang

Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)

Challenge:	Large language model (LLM)-integrated applications face security vulnerabilities from prompt injection (PI) attacks.
Approach:	They propose a model enhancement method that synthesizes diverse training data and employs instruction-level chain-of-thought fine-tuning to enable LLMs to effectively identify and reject malicious instructions regardless of their source or position in the context.
Outcome:	The proposed method outperforms baselines in three critical dimensions while maintaining utility performance without degradation.

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Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan

Text Classification

Challenge:	Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined.
Approach:	They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media.
Outcome:	The proposed model improves on the lifelong learning techniques on social media.

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

A Benchmark Dataset for Learning to Intervene in Online Hate Speech (D19-1)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, William Yang Wang

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)

Challenge:	Existing methods to detect online hate speech ignore conversational context . generative hate speech intervention is a novel approach to counter online hate .
Approach:	They propose a task where generative hate speech intervention generates responses to intervene during online conversations that contain hate speech.
Outcome:	The proposed method can detect and block hate speech and discourage it . it can also generate responses written by Mechanical Turk workers .

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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong

Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)

Challenge:	Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge.
Approach:	They propose a benchmark to evaluate domain specialization methods in real-world software development.
Outcome:	KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development.

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YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li, Hinrich Schuetze

DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear.
Approach:	They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs.
Outcome:	The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms.

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Jinglin Liu, Zhenhui Ye, Qian Chen, Siqi Zheng, Wen Wang, Zhang Qinglin, Zhou Zhao

Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026.findings-acl)

Challenge:	Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing.
Approach:	They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates .
Outcome:	The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation .

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Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Existing methods to address the "lost-in-the-middle" problem suffer from high latency or suboptimal hand-crafted scaling strategies.
Approach:	They propose a layer-specific positional embedding scaling method that assigns distinct scaling factors to each layer.
Outcome:	Experiments show that the proposed method mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks.

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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding (2023.findings-acl)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

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Moming Tang, Chengyu Wang, Jianing Wang, Chuanqi Tan, Songfang Huang, Cen Chen, Weining Qian

CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading (2024.emnlp-main)

Challenge:	Existing approaches to fine-tune visual-language understanding (VLU) require tasks-specific designs and sufficient training data.
Approach:	They propose a simple yet efficient paradigm for low-resource Visual Language Understanding (VLU) they reformulate a series of VLU tasks as an open-book affinity-matching problem.
Outcome:	The proposed framework outperforms baselines in low-resource settings.

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Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He

Responding E-commerce Product Questions via Exploiting QA Collections and Reviews (C18-1)

Challenge:	Large Language Models (LLMs) have been used for financial decision-making and stock market prediction for years.
Approach:	They propose to use Large Language Models to analyze on-chain and off-chain data to provide a comprehensive overview of the cryptocurrency market.
Outcome:	The proposed trading agent leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market.

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Qian Yu, Wai Lam, Zihao Wang

CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation (2023.findings-emnlp)

Challenge:	Existing QA and review collections can be used to provide instant responses to product questions . a proposed framework can be applied to a real-world commercial E-commerce site .
Approach:	They propose a framework for automatically responding product questions in E-commerce sites . existing QA pairs are exploited as distant supervision for learning to rank responses .
Outcome:	The proposed framework can return a ranked list of snippets serving as the automated response for a given question.

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Weixiang Yan, Yuchen Tian, Yunzhe Li, Qian Chen, Wen Wang

Multilinguality

Challenge:	Existing code translation datasets focus on a single pair of programming languages . early software systems are developed using programming languages such as Fortran and COBOL .
Approach:	They propose a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation.
Outcome:	The proposed framework supports translations between multiple programming languages and a cross-framework dataset for deep learning code across different frameworks.

Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (2025.emnlp-main)

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Shuo Xing, Peiran Li, Yuping Wang, Ruizheng Bai, Yueqi Wang, Chan-Wei Hu, Chengxuan Qian, Huaxiu Yao, Zhengzhong Tu

WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training (2026.findings-acl)

Challenge:	emergence of large Vision Language Models (VLMs) has broadened the capabilities of single-modal Large Language Model (LLM) but VLMs are prone to significant hallucinations, especially in the form of cross-modal inconsistencies.
Approach:	They propose a new alignment framework that leverages image retrieval to integrate both textual and visual preference signals.
Outcome:	The proposed framework mitigates hallucinations more effectively than previous methods . it maintains robustness and scalability across a wide range of VLM sizes and architectures .

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Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang, Yangzhuo Li, Ziqing Wang, Wen Wang, Jingyu Lu, Haoxiao Wang, Xueyi Pu, Fan Zhuo, Zhou Zhao

APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)

Challenge:	End-to-end spoken dialogue models have higher potential ceiling in expressiveness and perceptual ability than cascaded systems.
Approach:	They propose a modality-aware adaptive post-training recipe that constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring.
Outcome:	The proposed model improves speech quality and expressiveness across spoken dialogue benchmarks and architectures.

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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian

TrojFSP: Trojan Insertion in Few-shot Prompt Tuning (2024.naacl-long)

Challenge:	APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need.
Approach:	They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities .
Outcome:	The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy.

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Mengxin Zheng, Jiaqi Xue, Xun Chen, Yanshan Wang, Qian Lou, Lei Jiang

NLP+Code: Code Intelligence in Language Models (2025.emnlp-tutorials)

Challenge:	Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks.
Approach:	They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue.
Outcome:	The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA.

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Terry Yue Zhauo, Qian Liu, Zijian Wang, Wasi U. Ahmad, Binuian Hui, Loubna Ben Allal

Mitigating Hallucinations in VLMs: Enhancing Visual Attention via Head-Wise Perturbation (2026.findings-acl)

Challenge:	Language models have shown impressive abilities in a range of natural language processing tasks.
Approach:	This tutorial will provide an overview of the latest advances in natural language processing . it will provide preliminaries of training foundation models on code and their common practices .
Outcome:	This tutorial aims to provide an overview of recent advances in code modeling . it provides preliminaries of training foundation models on code and their common practices .

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Zhenghua Wang, Yixin Wu, Feiran Zhang, Qi Qian, Changze Lv, Xuanjing Huang, Xiaoqing Zheng

Improving Text Generation with Student-Forcing Optimal Transport (2020.emnlp-main)

Challenge:	Vision–Language Models (VLMs) have demonstrated strong capabilities in tasks that require joint understanding of text and images.
Approach:	They propose a strategy that incorporates head-wise attention perturbation via continuous multiplicative noise coupled with a visual-guided loss focused on vision-sensitive text tokens to promote a more balanced attention distribution.
Outcome:	The proposed approach outperforms baseline models on three benchmarks and consistently outperformed the baseline model.

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Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin

From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards (2026.findings-acl)

Challenge:	Maximum likelihood estimation (MLE) is used to train models, but during testing, the model is conditioned on previously generated tokens, resulting in exposure bias.
Approach:	They propose to use optimal transport to match the sequences generated in MLE and test modes to reduce exposure bias.
Outcome:	The proposed method is validated on machine translation, text summarization, and text generation tasks.

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Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos

Explainability & Interpretability in NLP

Challenge:	Existing explanations for large language models (LLMs) need to be able to verify outputs.
Approach:	They propose a method that constrains output communication to present a conclusion before its structured justification.
Outcome:	The proposed approach achieves 83.9% accuracy and correctness over CoT.

TeachMaster: Generative Teaching via Code (2026.acl-industry)

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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian

Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression (2026.findings-acl)

Challenge:	Existing methods for creating video content are limited by high costs and slow update cycles.
Approach:	They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome:	The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.

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Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)

Challenge:	Existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect.
Approach:	They propose to use a large-scale, context-aware speech corpus derived from multi-speaker audiobooks to generate a speech that is human-like.
Outcome:	The proposed model outperforms existing methods in terms of emotional expression accuracy and naturalness.

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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui

Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization (2023.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge.
Approach:	They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions.
Outcome:	The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks.

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Luyao Cheng, Siqi Zheng, Zhang Qinglin, Hui Wang, Yafeng Chen, Qian Chen

Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context (2025.emnlp-main)

Challenge:	Current speaker diarization systems consider only acoustic information, resulting in performance degradation when encountering adverse acustic environment.
Approach:	They propose methods to extract speaker-related information from conversational semantics in multi-party meetings.
Outcome:	The proposed method improves on AISHELL-4 and AliMeeting datasets on speakers diarization and speaker-turn detection.

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Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, Denghui Zhang

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

Challenge:	Incorporating external context can enhance the response quality of Large Language Models (LLMs). however, real-world contexts often mix relevant information with disproportionate inappropriate content.
Approach:	They propose a Poisoned Context Testbed to pair queries with real-world contexts . they propose 'rw-Steering' to internalize inappropriate signals .
Outcome:	The proposed model improves response quality by 39.8% and reverses undesirable behavior curve.

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Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation (2025.acl-long)

Challenge:	Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach:	They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome:	The proposed model is more effective in conversational machine comprehension with reinforcement learning.

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Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Qingshuang Bao, Weipeng Jiang, Qian Wang, Chao Shen, Yang Liu

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)

Challenge:	Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation.
Approach:	They propose to use a dataset to investigate a new type of bias in Large Language Models for code generation, provider bias, to determine whether the model favors specific providers.
Outcome:	The proposed model favors services from Google and Amazon, but without explicit directives, and can modify input code to incorporate their preferred providers without user requests.

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Qian Cao, Yahui Liu, Wei Bi, Yi Zhao, Ruihua Song, Xiting Wang, Ruiming Tang, Guorui Zhou, Han Li

Challenge:	Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity.
Approach:	They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation.
Outcome:	The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories.

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)

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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou

SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods (2025.acl-long)

Challenge:	Greek is the dominant language of the world's merchant navy and is a key language for international trade.
Approach:	They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap.
Outcome:	The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%.

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Wen Huang, Yanmei Gu, Zhiming Wang, Huijia Zhu, Yanmin Qian

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

Challenge:	Existing speech deepfake datasets are limited in scale and diversity, making it challenging to train models that can generalize well to unseen deepfakkes.
Approach:	They propose a large-scale speech deepfake dataset that includes over 3 million deepfak samples, totaling more than 3,000 hours of audio, generated using 40 different speech synthesis tools.
Outcome:	The proposed dataset includes over 3 million deepfake samples, totaling more than 3,000 hours of audio, generated using 40 different speech synthesis tools.

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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun

T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack (2020.emnlp-main)

Challenge:	Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains.
Approach:	They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries.
Outcome:	The proposed system outperforms baselines in the open domain task-solving benchmark.

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Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li

VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)

Challenge:	Existing adversarial examples can induce arbitrary errors to the target models, but they can be exploited to estimate robustness of NLP models.
Approach:	They propose a target-controllable adversarial attack framework T3 to handle adversarials . they use tree-based decoders to regularize the syntactic correctness of generated text .
Outcome:	The proposed framework can be used to estimate the robustness of NLP models.

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Kun Qian, Shunji Wan, Claudia Tang, Youzhi Wang, Xuanming Zhang, Maximillian Chen, Zhou Yu

Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs (2024.findings-emnlp)

Challenge:	Recent benchmarks release only training and validation sets, keeping the test set labels closed-source.
Approach:	They propose to extract variables from each test case and define a value range for each variable.
Outcome:	The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks.

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Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, Qian Wang

An End-to-End Generative Architecture for Paraphrase Generation (D19-1)

Challenge:	Existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.
Approach:	They propose to use Context-Driven Index Trimming framework to capture and regulate consistency between retrieved contexts and modify indexes in the database.
Outcome:	Experiments show that the proposed framework can improve answer quality by 3.75% on open-domain question-answering tasks.

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Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, Lawrence Carin