Papers by Qing Wang

From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection (2025.naacl-long)

Challenge:	Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs, but it still requires professional knowledge to facilitate the expertise for some domain- specific tasks.
Approach:	They propose a pipeline to solve domain-specific calculation problems with KIPG . they use it to extract key variables and calculate outcomes dependent on domain knowledge .
Outcome:	The proposed pipeline solves domain-specific calculation problems more effectively . it generates knowledge-intensive programs according to the domain- specific documents .

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Rupeng Zhang, Haowei Wang, Junjie Wang, Mingyang Li, Yuekai Huang, Dandan Wang, Qing Wang

CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation (2026.acl-long)

Challenge:	Toolcalling has changed Large Language Model (LLM) applications by integrating external tools, but it also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively studied.
Approach:	They propose a framework that exploits vulnerabilities in Large Language Models through adversarial tool injection to execute privacy theft, launch denial-of-service attacks, and manipulate business competition.
Outcome:	The proposed framework exploits vulnerabilities in LLM tool-calling systems through adversarial tool injection.

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Tianxin Han, Qing Dong, Xingwei Wang, Jie Jia, Gang Wu, Bowen Yang, Fu Zhang

Recurrent Attention Networks for Long-text Modeling (2023.findings-acl)

Challenge:	Existing methods for camouflaged object segmentation are limited to vision-only mask prediction under fixed task assumptions.
Approach:	They propose a language-guided reasoning camouflaged object segmentation task that generates an intent-consistent segmentation mask from an image and an implicit query text instruction.
Outcome:	The proposed task can generate an intent-consistent segmentation mask from a camouflaged image and an implicit query text instruction.

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Xianming Li, Zongxi Li, Xiaotian Luo, Haoran Xie, Xing Lee, Yingbin Zhao, Fu Lee Wang, Qing Li

Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)

Challenge:	Existing approaches to encoding long documents using self-attention have been limited by quadratic computational complexities and limited application in long text processing.
Approach:	They propose a long-document encoding model that allows the recurrent operation of self-attention.
Outcome:	The proposed model extracts global semantics in token-level and document-level representations, making it inherently compatible with both sequential and sequential tasks.

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Zhiyuan Chang, Mingyang Li, Junjie Wang, Yi Liu, Qing Wang, Yang Liu

Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)

Challenge:	Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach:	They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome:	The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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Xin Wu, Yi Cai, Yang Kai, Tao Wang, Qing Li

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs (2025.acl-long)

Challenge:	Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets.
Approach:	They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings.
Outcome:	The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods.

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Yuchen Fu, Zifeng Cheng, Zhiwei Jiang, Zhonghui Wang, Yafeng Yin, Zhengliang Li, Qing Gu

Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization (2026.findings-acl)

Challenge:	Recent studies have focused on prompt engineering to extract sentence embeddings from large language models (LLMs) but these models are mostly decoder-only and the earlier tokens in the sentence cannot attend to the latter, resulting in biased encoding of sentence information and cascading effects on the final decoded token.
Approach:	They propose a plug-and-play and training-free technique that prepends each layer’s decoded sentence embedding to the beginning of the sentence in the next layer’ s input.
Outcome:	The proposed technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs while incurring negligible additional inference cost.

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Jie Huang, Junjie Wang, Xin Liao, Ziyou Jiang, Wenshuo Wang, Shoubin Li, Qing Wang

Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)

Challenge:	Existing text-to-image retrieval methods suffer from limited semantic discriminability, alignment bias, and closed-set restrictions.
Approach:	They propose a framework for semantic internalization for Generative Multimodal Alignment . they construct multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations .
Outcome:	The proposed framework outperforms state-of-the-art frameworks on Flickr30K and MS-COCO datasets . it achieves average Recall@1, Recall @5, and Recall_10 improvements of 10.65%, 8.50%, and 7.00% .

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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement (2026.acl-long)

Challenge:	Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation.
Approach:	They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task.
Outcome:	The proposed model improves inference speed by 2.3-2.7x times without compromising model performance.

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Xinmeng Hou, Bohao Qu, Wuqi Wang, Peiliang Gong, Qing Guo, Yang Liu

Green & Sustainable NLP

Challenge:	Existing self-improving frameworks rely on inefficient, multi-turn recursive loops that incur high computational costs.
Approach:	They propose a framework that achieves efficient self-evolution within a single recurrence cycle.
Outcome:	The proposed framework outperforms state-of-the-art self-evolving systems while significantly reducing computational overhead.

Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)

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Lin Zhong, Siyu Zhu, Zizhen Yuan, Jinhao Cui, Xinyang Zhao, Lingzhi Wang, Hao Chen, Qing Liao

Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)

Challenge:	Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection.
Approach:	They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states.
Outcome:	The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure.

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Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, Mu Li

Text Classification

Challenge:	Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text.
Approach:	They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context.
Outcome:	The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

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Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks.

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Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, Qing Li

DEFT: Demystifying VLN Failures via a Unified Dual-View Explainability Framework for LLM-based Agents (2026.acl-long)

Challenge:	E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications .
Approach:	They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding .
Outcome:	The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications.

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Yawen Wang, Yihan Dai, Jianming Chen, Junjie Wang, Qing Wang

Explainability & Interpretability in NLP

Challenge:	Existing interpretability methods isolate temporal criticality from feature salience, creating an alignment gap and failing to account for the behavioral instability of black-box agents.
Approach:	They propose a unified dual-view framework that jointly analyzes when a decision is pivotal and what visual evidence grounds it.
Outcome:	Extensive experiments on MatterPort3D show that DEFT outperforms baselines in both temporal and feature fidelity.

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Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu

When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)

Challenge:	Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer.
Approach:	They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions.
Outcome:	The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios.

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Liang-Bo Ning, Yuchen Zhu, Heqing Huang, Xin Wang, Yi Chang, Li Qing, Wenqi Fan

Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)

Challenge:	Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost.
Approach:	They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations.
Outcome:	The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations.

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Hua Cai, Xuli Shen, Qing Xu, Weilin Shen, Xiaomei Wang, Weifeng Ge, Xiaoqing Zheng, Xiangyang Xue

Dialogue Systems & Conversational Agents

Challenge:	Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful.
Approach:	They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation.
Outcome:	The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses.

Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs (2024.emnlp-main)

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Chengyuan Liu, Shihang Wang, Lizhi Qing, Kun Kuang, Yangyang Kang, Changlong Sun, Fei Wu

Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring (2023.acl-long)

Challenge:	Large Language Models (LLMs) struggle when it comes to specialized domains due to limited domain-specific knowledge.
Approach:	They propose an adaptive method that automatically identifies valuable words from a given domain vocabulary.
Outcome:	The proposed method has been validated on three Chinese datasets and performed on general tasks.

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Cong Wang, Zhiwei Jiang, Yafeng Yin, Zifeng Cheng, Shiping Ge, Qing Gu

Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)

Challenge:	Automated Essay Scoring (AES) aims to evaluate the quality score of input essays without human intervention.
Approach:	They propose an unsupervised approach to evaluate the quality of input essays . they use multiple heuristic quality signals as pseudo-groundtruths to train a neural AES model .
Outcome:	The proposed approach achieves state-of-the-art performance on eight prompts of ASPA dataset compared with previous unsupervised methods .

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Heyan Chai, Ziyi Yao, Siyu Tang, Ye Wang, Liqiang Nie, Binxing Fang, Qing Liao

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

Challenge:	Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence.
Approach:	They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information.
Outcome:	The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness.

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

GenDecider: Integrating “None of the Candidates” Judgments in Zero-Shot Entity Linking Re-ranking (2024.naacl-short)

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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Kang Zhou, Yuepei Li, Qing Wang, Qiao Qiao, Qi Li

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media (2025.emnlp-main)

Challenge:	Existing methods for zero-shot reranking assume the correct entity is always among the retrieved candidates.
Approach:	They propose a novel re-ranking approach for Zero-Shot Entity Linking . they use the Llama model to detect scenarios where the correct entity is not retrieved .
Outcome:	The proposed approach significantly improves disambiguation and accuracy on the ZESHEL dataset.

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Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu

Large-Scale Multimodal Knowledge Graph about Classical Chinese Poetry: Fine-grained Method and Comprehensive Evaluation (2026.findings-acl)

Challenge:	Social media is a key platform for emotional expression, yet deep learning lacks flexibility and interpretability.
Approach:	They propose to use Chinese social media to train interpretable mental health instruction datasets to test models' ability to explain their decisions.
Outcome:	The proposed models outperform deep learning and LLMs on three mental health downstream tasks and demonstrate their potential for clinical applications.

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Shuo Wang, Qing Zhu, Yang Xiao, Minglong Lei

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)

Challenge:	Existing studies on classical Chinese poetry are limited by modality constraints, dataset size, or the level of refinement.
Approach:	They propose to construct a large-scale and fine-grained multimodal knowledge graph of classical Chinese poetry using an informative ontology graph and a text-image alignment method.
Outcome:	The proposed method collects knowledge about classical Chinese poetry from ontology graphs and performs four tasks that demonstrate its comprehensiveness and high quality.

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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang

Dialogue Systems & Conversational Agents

Challenge:	Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability.
Approach:	They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations .
Outcome:	The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient .

Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)

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Jia Du, Xuanyu Zhang, Siyi Wang, Kai Wang, Yanquan Zhou, Lei Li, Qing Yang, Dongliang Xu

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

Challenge:	Few-shot text matching is a more practical technique to determine whether two texts are semantically identical.
Approach:	They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens .
Outcome:	The proposed method outperforms baselines on MRPC and QQP.

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

Multilinguality

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

LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design (2026.findings-acl)

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Leshu Li, An Lu, Haiyu Wang, Zhibin Feng, Conghui Duan, Qing Bao, Zongmin Zhao, Sai Qian Zhang

Green & Sustainable NLP

Challenge:	Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are effective and biologically safe remains a major bottleneck.
Approach:	They propose a safety-aware multi-agent LLM framework for lipid discovery that enforces toxicity as a prerequisite for efficiency prediction.
Outcome:	The proposed framework achieves an average improvement in mRNA transfection efficiency prediction across multiple foundation models.

ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)

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Jiani Huang, Shijie Wang, Liang-Bo Ning, Wenqi Fan, Li Qing

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

Challenge:	Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants .
Approach:	They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics .
Outcome:	The proposed framework outperforms state-of-the-art recommendations and preserves core abilities.

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)

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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)

Challenge:	Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types.
Approach:	They propose a novel retrieval method that integrates specialized knowledge into LLMs.
Outcome:	The proposed method can perform multiple legal retrieval tasks for LLMs.

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Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs.
Approach:	They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.
Outcome:	The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

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

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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

Neural Mixed Counting Models for Dispersed Topic Discovery (2020.acl-main)

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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Jiemin Wu, Yanghui Rao, Zusheng Zhang, Haoran Xie, Qing Li, Fu Lee Wang, Ziye Chen

Distillation with Explanations from Large Language Models (2024.lrec-main)

Challenge:	Existing methods for inference of parameter parameters are time-consuming and difficult to use.
Approach:	They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery.
Outcome:	The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets.

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Hanyu Zhang, Xiting Wang, Xiang Ao, Qing He

More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs (2024.emnlp-main)

Challenge:	Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers.
Approach:	They propose to use Large language models (LLMs) to generate more accurate answers and corresponding free-text explanations by combining ground truth labels and answers-explanations generated by LLMs.
Outcome:	The proposed method achieves improved predictive performance and generates explanations that exhibit greater alignment with the model’s task outputs.

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Chengyuan Liu, Yangyang Kang, Shihang Wang, Lizhi Qing, Fubang Zhao, Chao Wu, Changlong Sun, Kun Kuang, Fei Wu

Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)

Challenge:	a recent study shows that performance on general tasks decreases after Large Language Models are fine-tuned on domain-specific tasks.
Approach:	They propose a general capability integration approach to integrate general capabilities and domain knowledge within a single instance.
Outcome:	The proposed method improves performance on domain-specific tasks by integrating general capabilities and domain knowledge.

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Qiyao Peng, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang

Improving Deep Embedded Clustering via Learning Cluster-level Representations (2022.coling-1)

Challenge:	Existing approaches to expert finding are effective for a community question answering platform.
Approach:	They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations.
Outcome:	The proposed framework could learn more comprehensive question representations on six real-world datasets.

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Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang

Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)

Challenge:	Existing efforts to learn meaningful representations at the instance level are limited.
Approach:	They propose a deep embedded clustering model with cluster-level representation learning to jointly learn cluster and instance level representations.
Outcome:	The proposed model produces meaningful clusters on real-world short text datasets.

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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

Challenge:	Existing direct speech-to-speech translation models rely on text generation as an intermediate step.
Approach:	They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation.
Outcome:	The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision.

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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System (2025.acl-long)

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Ziyou Jiang, Mingyang Li, Guowei Yang, Junjie Wang, Yuekai Huang, Zhiyuan Chang, Qing Wang

CARE-CR: Context-Aware Routing and Expert Fusion for Multi-Preference Cognitive Restructuring (2026.findings-acl)

Challenge:	Existing approaches to attack Large Language Model (LLM) tool-learning systems are black-box oriented and rely on static commands that cannot adapt flexibly to the changes in user queries and the invocation chain.
Approach:	They propose a dynamic attack comment generation approach for information theft attacks in LLM tool-learning systems that mimics the familiar by inferring the information utilized by upstream tools.
Outcome:	The proposed approach outperforms baselines with +13.2% ASRTheft and can be generalized to new tool-learning systems to expose their information leakage risks.

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Hongzhi Qi, Liangcheng Wang, Yijing YU, Jianqiang Li, Bing Xiang Yang, Qing Zhao

Cognitive Modeling

Challenge:	Large Language Models (LLMs) offer promising avenues for automated cognitive restructuring in mental health settings, but current approaches lack the adaptability to balance conflicting therapeutic dimensions, such as empathy and rationality.
Approach:	They propose a decoupled optimization framework that implements a dimension-guided Monte Carlo tree search to train expert policies specialized for distinct therapeutic attributes rather than relying on a monolithic alignment strategy.
Outcome:	The proposed framework achieves consistent improvements over baselines across multiple evaluation dimensions, including diagnostic accuracy, contextual appropriateness, task effectiveness, and overall helpfulness, while enabling controllable cognitive restructuring generation.

A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)

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Xinhong Chen, Qing Li, Jianping Wang

A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020.coling-main)

Challenge:	Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors .
Approach:	They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished.
Outcome:	The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion.

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Haopeng Ren, Yi Cai, Xiaofeng Chen, Guohua Wang, Qing Li

CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)

Challenge:	Existing supervised and distantly supervised RC models ignore the emergence of novel relations in open environment.
Approach:	They propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances without catastrophic forgetting.
Outcome:	Experiments show that the proposed model performs better on deep learning and few-shot learning . it can recognize the novel relations with a few support instances without catastrophic forgetting .

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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang

VAEGPT-Sim: Improving Sentence Representation with Limited Corpus Using Gradually-Denoising VAE (2024.findings-acl)

Challenge:	Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries .
Approach:	They propose a framework that facilitates the coevolution of large language models and retrieval models.
Outcome:	The proposed framework facilitates the coevolution of LLMs and retrieval models.

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Zhenyi Wang, Haiyan Ning, Qing Ling, Dan Wang

CoRec: An Easy Approach for Coordination Recognition (2023.emnlp-main)

Challenge:	Text embedding requires a highly efficient method for training domain-specific models on limited corpora.
Approach:	They propose a model that combines a denoising variational autoencoder with a target-specific discriminator to generate synonymous sentences that closely resemble human language.
Outcome:	The proposed model surpasses ConSERT by 2.8 points in small-dataset training on STS benchmarks.

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Qing Wang, Haojie Jia, Wenfei Song, Qi Li

Syntactic Text Processing

Challenge:	Existing syntactic parsers are slow and suffer from errors, especially for long and complicated sentences.
Approach:	They propose a pipeline model COordination RECognizer with coordinator identifier and conjunct boundary detector.
Outcome:	The proposed model improves the yield of state-of-the-art Open IE models by reducing errors and slow processing time.

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang

R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’ (2024.naacl-long)

Challenge:	Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach:	They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
Outcome:	The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems.

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Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang

AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)

Challenge:	Existing methods for instruction tuning force the model to complete a sentence no matter whether it knows the knowledge or not.
Approach:	They propose a new approach to tuning large language models to refrain from answering questions beyond its parametric knowledge by identifying the disparity in parametric and parametric information.
Outcome:	The proposed approach improves a model’s ability to answer known questions and refrain from answering unknown questions.

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Kaize Shi, Xueyao Sun, Li He, Dingxian Wang, Qing Li, Guandong Xu

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering (2025.acl-long)

Challenge:	Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input.
Approach:	They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting.
Outcome:	The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations.

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Zifeng Cheng, Zhonghui Wang, Yuchen Fu, Zhiwei Jiang, Yafeng Yin, Cong Wang, Qing Gu

TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route (2025.emnlp-main)

Challenge:	Existing studies focus on prompt engineering to encode the full semantics of a sentence into the embedding of the last token.
Approach:	They propose a technique that introduces an extra auxiliary prompt to elicit better sentence embedding . they propose to use the hidden state of the token as the sentence embedded in LLMs .
Outcome:	The proposed technique can improve performance of existing prompt-based methods on STS tasks and downstream classification tasks.

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Hongyi Luo, Qing Cheng, Daniel Matos, Hari Krishna Gadi, Yanfeng Zhang, Lu Liu, Yongliang Wang, Niclas Zeller, Daniel Cremers, Liqiu Meng

Conditional Causal Relationships between Emotions and Causes in Texts (2020.emnlp-main)

Challenge:	Existing studies on large language models have limited evaluation of their geospatial cognition . a unified framework for evaluating geospcial cognition in LLMs remains absent .
Approach:	They propose a benchmark to evaluate the geospatial route cognition of Large Language Models . they propose 'pathbuilder' tool for converting natural language instructions into navigation routes .
Outcome:	The proposed framework and metrics evaluate 9 state-of-the-art LLMs on route reversal task.

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Xinhong Chen, Qing Li, Jianping Wang

Sentiment Analysis

Challenge:	Existing studies on the causal relationships between emotions and causes focus on extracting causally related clauses from documents, but none considers whether context clauses are indispensable for extracted clauses to be causally linked.
Approach:	They propose a task to determine whether an input pair of emotion and cause has a valid causal relationship under different contexts.
Outcome:	The proposed task identifies whether an input pair of emotion and cause has a valid causal relationship under different contexts and then fine-tunes the prediction results based on the characteristics of the input clauses.

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

Investigating Context Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style (2025.findings-acl)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li

AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)

Challenge:	Retrieval-augmented generation improves Large Language Models (LLMs) by integrating external information into the response generation process.
Approach:	They investigate the impact of memory strength and evidence presentation on LLMs’ receptiveness to external evidence by measuring the divergence in LLM responses to different paraphrases of the same question.
Outcome:	The proposed method improves Large Language Models (LLMs) by integrating external information into the response generation process.

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Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Explainability & Interpretability in NLP

Challenge:	Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored.
Approach:	They propose a framework AbsInstruct to enhance LLMs’ abstract ability through instruction tuning.
Outcome:	The proposed framework can enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)

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Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao

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

Challenge:	Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks.
Approach:	They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms.
Outcome:	The proposed framework eliminates the need for user alignment between platforms.

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

Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems (2026.acl-long)

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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Mengzhuo Chen, Junjie Wang, Fangwen Mu, Yawen Wang, Zhe Liu, Huanxiang Feng, Qing Wang

ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty (2025.findings-acl)

Challenge:	Existing benchmarks rely on partially observable traces that capture only agent outputs . lack of full execution traces obscures many failure causes, authors argue .
Approach:	They propose a benchmark that allows attribution under full execution observability . they find full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart .
Outcome:	The proposed benchmark improves attribution accuracy by up to 76.5% over a partial-observation counterpart.

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Qing Zong, Zhaowei Wang, Tianshi Zheng, Xiyu Ren, Yangqiu Song

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

Challenge:	Existing studies on LLMs' factual knowledge are unreliable since the questions can vary not only in entity frequency but also in difficulty themselves.
Approach:	They propose a benchmark to study the role of knowledge frequency in the performance of large language models (LLMs) it aims to avoid possible semantic shortcuts which is a serious problem of current QA study.
Outcome:	The proposed method avoids possible semantic shortcuts and improves on existing proofs.

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

Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)

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

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Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan, Chunjie Yang, Qing Cui, Jun Zhou

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models (2025.acl-long)

Challenge:	Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets.
Approach:	They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC).
Outcome:	The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset.

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Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, WangYan WangYan, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)

Challenge:	Large language models face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.
Approach:	They propose a training strategy for extending the context window of LLMs including impactful token analysis, position index transformation, and training optimization strategies.
Outcome:	Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size.

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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang

Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)

Challenge:	Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries.
Approach:	They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.
Outcome:	The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.

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Xinting Zhang, Mengqiu Cheng, Mengzhen Wang, Songwen Gong, Jiayuan Xie, Yi Cai, Qing Li

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

Challenge:	Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word.
Approach:	They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query.
Outcome:	The proposed model significantly outperforms existing methods across multiple programming language datasets.

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, Fakhri Karray

Visual Data in NLP

Challenge:	Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries.
Approach:	They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs.
Outcome:	The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios.

All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction (2026.acl-long)

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Ziyou Jiang, Mingyang Li, Junjie Wang, Yuekai Huang, Jie Huang, Zhiyuan Chang, Zhaoyang Li, Qing Wang

Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)

Challenge:	Existing methods for harmful meme detection only learn the combination of harmful elements and lack understanding of these implicit expressions.
Approach:	They propose a method that detects harmful memes by replicating the design concept of malicious users.
Outcome:	The proposed method achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes.

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Rubing Chen, Jiaxin Wu, Jian Wang, Xulu Zhang, Wenqi Fan, Chenghua Lin, Xiaoyong Wei, Li Qing

Question Answering

Challenge:	Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness.
Approach:	They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness.
Outcome:	The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields.

Towards a More Generalized Approach in Open Relation Extraction (2025.acl-long)

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Qing Wang, Yuepei Li, Qiao Qiao, Kang Zhou, Qi Li

HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)

Challenge:	Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances.
Approach:	They propose a generalized OpenRE setting that considers unlabeled data as a mixture of known and novel instances.
Outcome:	The proposed framework outperforms baselines in relation classification and clustering on three benchmark datasets.

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Qing Li, Jiahui Geng, Zongxiong Chen, Derui Zhu, Yuxia Wang, Congbo Ma, Chenyang Lyu, Fakhri Karray

PENS: A Dataset and Generic Framework for Personalized News Headline Generation (2021.acl-long)

Challenge:	Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability.
Approach:	They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements.
Outcome:	The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods.

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Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, Xing Xie

Text Generation

Challenge:	Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines.
Approach:	They propose a generic framework to personalize a news headline generator and establish personalized headlines by leveraging user behavioral data.
Outcome:	The proposed framework is based on user preference data and user preference injections to personalize a text generator and establish personalized headlines.

STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing (2025.findings-acl)

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Jiaru Zou, Qing Wang, Pratyush Thakur, Nickvash Kani

Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)

Challenge:	Advances in large language models have spurred research into enhancing their reasoning capabilities, particularly in math-rich STEM documents.
Approach:	They propose a benchmark dataset to evaluate LLMs’ reasoning abilities on math symbols within contextual scientific text.
Outcome:	The proposed dataset demonstrates that state-of-the-art LLMs achieve an average accuracy of 20-60% under in-context learning and 50-60% with fine-tuning, highlighting a substantial gap in their ability to classify mathematical symbols.

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Heyan Chai, Jinhao Cui, Ye Wang, Min Zhang, Binxing Fang, Qing Liao

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

Challenge:	Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks .
Approach:	They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks.
Outcome:	The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training.

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

Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents (2026.findings-acl)

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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Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Shoubin Li, Qing Wang, Fanjiang Xu

Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis (2024.findings-acl)

Challenge:	Existing testing methods are system-level and provide limited insight into which capability deficiencies cause task failures.
Approach:	They propose a capability-oriented testing approach that enables failure detection and attribution by seed selection and mutation.
Outcome:	The proposed method detects more failure cases and pinpoints capability-level deficiencies than state-of-the-art baselines, providing more interpretable and actionable guidance for improving embodied agents.

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Wei Zhai, Hongzhi Qi, Qing Zhao, Jianqiang Li, Ziqi Wang, Han Wang, Bing Yang, Guanghui Fu

GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)

Challenge:	Existing models for language analysis are inadequate for specialized domains like psychology.
Approach:	They have enriched a Chinese social media database with psychological lexicons to enhance its applicability to psychological text analysis.
Outcome:	The proposed model performed better on six public datasets and provided relevant predictions given the masked sentences.

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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao

Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty? (2025.acl-short)

Challenge:	Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations .
Approach:	a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space .
Outcome:	GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models .

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Jiayu Liu, Qing Zong, Weiqi Wang, Yangqiu Song

SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)

Challenge:	Large language models (LLMs) are increasingly used in high-stakes domains, but their confidence is inconsistent in out-of-distribution scenarios.
Approach:	They define "marker confidence" as the observed accuracy when a model employs an epistemic marker.
Outcome:	The proposed model generalizes well within the same distribution, but its confidence is inconsistent in out-of-distribution scenarios.

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Jie Huang, Xin Liao, Junjie Wang, Mingyang Li, Wenshuo Wang, Ziyou Jiang, Shoubin Li, Qing Wang

Visual Data in NLP

Challenge:	Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution .
Approach:	They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics .
Outcome:	The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks.

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)

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Lin Zhong, Renjin Zhu, Shujuan Ma, Jinhao Cui, Lingzhi Wang, Hao Chen, Qing Liao

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level.
Approach:	They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers.
Outcome:	The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

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

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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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

SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)

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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Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu

AEA: Adaptive Expert Allocation Improves Sentence Embeddings from Mixture-of-Experts LLM (2026.acl-long)

Challenge:	Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs.
Approach:	They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide .
Outcome:	The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data.

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Shufan Yang, Zifeng Cheng, Zhiwei Jiang, Qingfeng Qi, Yafeng Yin, Cong Wang, Ao Zhou, Qing Gu

Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.emnlp-main)

Challenge:	Existing methods to improve embeddings from Mixture-of-Experts models allocate a fixed number of experts uniformly across all layers and tokens, ignoring inter-layer and inter-token heterogeneity.
Approach:	They propose an Adaptive Expert Allocation framework that performs layer-wise and token-wise expert allocation to enhance embedding quality.
Outcome:	The proposed method improves embedding quality across multiple MoE models.

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Qing Wang, Kang Zhou, Qiao Qiao, Yuepei Li, Qi Li

Planning-Aware Code Infilling via Horizon-Length Prediction (2025.emnlp-main)

Challenge:	Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects.
Approach:	They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning.
Outcome:	The proposed task increases diversity of positive pairs and strengthens discriminative power . it overcomes limitations of traditional Relation Extraction tasks, which require manual annotations .

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Yifeng Ding, Hantian Ding, Shiqi Wang, Qing Sun, Varun Kumar, Zijian Wang

Challenge:	Current approaches to fill-in-the-middle (FIM) often fail to generate content that aligns well with the surrounding context.
Approach:	They propose a training objective that teaches models to predict the number of remaining middle tokens at each step.
Outcome:	The proposed training objective improves FIM performance by up to 24% on diverse benchmarks across file-level and repository-level.

Pre-trained Personalized Review Summarization with Effective Salience Estimation (2023.findings-acl)

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Hongyan Xu, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang

Summarization

Challenge:	Pretrained language models (PLMs) are a new paradigm in text generation for the strong ability of natural language comprehension.
Approach:	They propose a pre-trained personalized review summarization method that incorporates personalized information into the salience estimation of input reviews.
Outcome:	The proposed method performs better than the state-of-the-art methods on real-world datasets.

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

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

QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)

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

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Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang

Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification (2025.findings-acl)

Challenge:	Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken.
Approach:	They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages.
Outcome:	The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs.

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Wei Xiang, Chuanhong Zhan, Qing Zhang, Bang Wang

Large Language Models are Complex Table Parsers (2023.emnlp-main)

Challenge:	Existing studies focus on identifying existence of causality between two event mentions, but the direction of causalities is crucial for understanding the causal relation.
Approach:	They propose to instruct a GLM to generate causality statements and identify directional event causality by evaluating the generated statements.
Outcome:	The proposed method significantly outperforms state-of-the-art methods even with fewer training data.

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Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He, Yingwen Wang, Qing Wang, Rui Feng, Xiaobo Zhang

Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation (2026.findings-acl)

Challenge:	Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets.
Approach:	They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues.
Outcome:	The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance.

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Zhen Bi, Zhenlin Hu, Xueshu Chen, Mingyang Chen, Cheng Deng, Yida Xue, Zhen Wang, Qing Shen, Ningyu Zhang, Jungang Lou

Multi-Prompting Decoder Helps Better Language Understanding (2025.findings-acl)

Challenge:	Existing data-centric paradigms equate quality with factuality or diversity and ignore the internal logical complexity of training samples.
Approach:	They propose a density-aware re-cognizing optimization strategy that prioritizes high-density logical samples to align training with the model's reasoning boundary.
Outcome:	The proposed metric outperforms existing methods and improves reasoning performance without increasing total data volume.

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Zifeng Cheng, Zhaoling Chen, Zhiwei Jiang, Yafeng Yin, Cong Wang, Shiping Ge, Qing Gu

CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)

Challenge:	Existing methods to adapt Pre-trained Language Models to downstream tasks are limited by their inference APIs.
Approach:	They propose a multi-prompting decoding framework that query PLMs with multiple prompts . they propose to query Plms with optimal transport for hidden states and calibrated decoding for class scores .
Outcome:	The proposed framework achieves state-of-the-art results on multiple natural language understanding datasets under the few-shot setting.

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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin

Green & Sustainable NLP

Challenge:	Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up.
Approach:	They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning.
Outcome:	The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets.

SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models (2024.acl-long)

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Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)

Challenge:	Existing methods to address catastrophic forgetting and knowledge transfer in large language models (LLMs) ignore potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfers simultaneously.
Approach:	They propose a Shared Attentive Learning & Selection module to align the PET learning and selection modules to address catastrophic forgetting and knowledge transfer simultaneously.
Outcome:	Experiments on two CL benchmarks show that the proposed framework is superior when scaled to different model sizes, different model architectures and unseen tasks.

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Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, Qing Guo

Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)

Challenge:	Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted.
Approach:	They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage.
Outcome:	The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks.

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Xianzhen Luo, Yixuan Wang, Qingfu Zhu, Zhiming Zhang, Xuanyu Zhang, Qing Yang, Dongliang Xu

Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization (2025.acl-long)

Challenge:	Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency.
Approach:	They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree.
Outcome:	The proposed method outperforms existing train-free methods by 30% and even a training method by 25%.

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Yihao Huang, Chong Wang, Xiaojun Jia, Qing Guo, Felix Juefei-Xu, Jian Zhang, Yang Liu, Geguang Pu

Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach (2025.coling-main)

Challenge:	Existing methods for prompt injection have focused on optimizing the suffix, overlooking the role of the prompt.
Approach:	They propose a method that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies to optimize the suffix sequence for universal goal hijacking.
Outcome:	The proposed method can generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking.

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Yuepei Li, Kang Zhou, Qiao Qiao, Qing Wang, Qi Li