Papers by Jun Sun
Unleashing the Unseen: Harnessing Benign Datasets for Jailbreaking Large Language Models (2026.findings-eacl)
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| Challenge: | Despite significant efforts in safety alignment, large language models (LLMs) such as GPT-4 and LLaMA 3 remain vulnerable to jailbreak attacks that can induce harmful behaviors. |
| Approach: | They propose a feature extraction method to extract sample-agnostic features from benign datasets in the form of adversarial suffixes and propose 'suffix maybe features' they show that adversarials generated from jailbreak attacks may contain meaningful features, i.e. appending the same suffix to different prompts results in responses exhibiting specific characteristics. |
| Outcome: | The proposed method extracts sample-agnostic features from benign datasets and shows that they may contain meaningful features. |
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)
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Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, Mingming Sun
| Challenge: | Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed . |
| Approach: | They propose a fine-grained semantic matching method tailored for zero-shot relation extraction . they decompose sentence-level similarity score into entity matching score and context matching score . |
| Outcome: | The proposed method achieves higher matching accuracy and faster inference speed than state-of-the-art methods. |
Continual Few-shot Event Detection via Hierarchical Augmentation Networks (2024.lrec-main)
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| Challenge: | Existing methods for continual few-shot event detection use labeled data, but in real-world applications, new event types emerge continually. |
| Approach: | They propose a memory-based framework for continual few-shot event detection . they incorporate prototypical augmentation into the memory set to memorize previous event types . |
| Outcome: | The proposed method outperforms existing methods in multiple continual few-shot event detection tasks. |
LEGO: A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023.findings-emnlp)
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| Challenge: | Causality explanation generation is a generative task that aims to explain why a given cause-effect pair is true using natural language. |
| Approach: | They propose a multi-agent framework with role-playing and iterative feedback for causality explanation generation. |
| Outcome: | The proposed framework is superior to existing frameworks on WIKIWHY and e-CARE datasets. |
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
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| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)
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Jun Sun, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang, Shu-Kai Zheng, Yulong Liu, Yuxin Huang, Taihao Li
| Challenge: | Existing studies focus on developing models that exploit the unification of multiple modalities. |
| Approach: | They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities. |
| Outcome: | The proposed model outperforms state-of-the-art models in multi-modal emotion recognition. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate (2025.findings-acl)
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| Challenge: | Existing rule retrieval methods suffer from low accuracy due to semantic gap between instantiated facts and abstract representations of rules. |
| Approach: | They propose a method that induces inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. |
| Outcome: | The proposed method improves retrieval effectiveness and accuracy across settings. |
De-Biased Court’s View Generation with Causality (2020.emnlp-main)
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Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu
| Challenge: | Existing approaches to court’s view generation can be used to address this problem, but neglecting the confounding bias in data can limit the model performance and pollute learning outcomes. |
| Approach: | They propose a novel Attentional and Counterfactual based Natural Language Generation method consisting of an attentional encoder and a pair of innovative counterfactual decoders to generate judgment-discriminative court's views. |
| Outcome: | The proposed method is able to generate judgment-discriminative court's views (both supportive and non-supportive views) under both quantitative and qualitative evaluation metrics. |
Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)
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| Challenge: | Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications. |
| Approach: | They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level. |
| Outcome: | The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance. |
ExpNote: Black-box Large Language Models are better Task Solvers with Experience Notebook (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown great power in solving various tasks but fail in many specific tasks. |
| Approach: | They propose a framework to help black-box LLMs better adapt to unfamiliar tasks by reflecting and noting experiences from training data and retrieving them from external memory during testing. |
| Outcome: | The proposed framework improves the performance of black-box Large Language Models on multiple tasks and demonstrates that it is a good choice for the future. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
Listening Like Humans: Semantics-Guided Noise-Robust Multimodal Speech Recognition (2026.acl-long)
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| Challenge: | Severe acoustic degradation results in unreliable ASR outputs . et al., 2024b): critical concerns regarding reliability and fairness of ASR . |
| Approach: | They propose a multimodal framework that reframes ASR as semantics-guided speech reconstruction. |
| Outcome: | The proposed framework achieves an average reduction in WER while also attaining 98.71% BERTScore and 96.7% USE over advanced baselines. |
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing relation extraction methods require centralizing training data from different medical platforms while holding the privacy-sensitive data puts patients' privacy at risk. |
| Approach: | They propose a federated relation extraction model that trains a central model without sharing or exchange of private local data. |
| Outcome: | The proposed model trains a central model without uploading local parameters, and it performs well on three publicly available datasets. |
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)
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| Challenge: | Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient. |
| Approach: | They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs . |
| Outcome: | The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs . |
Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)
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Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy. |
| Approach: | They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information. |
| Outcome: | The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. |
| Approach: | They propose a data-level defence that renders text unlearnable to LLMs by injecting carefully designed alignment-triggering disclaimers into the models' alignment mechanisms. |
| Outcome: | The proposed approach exploits the models’ alignment mechanisms to prevent effective learning. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.acl-long)
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| Challenge: | Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation. |
| Approach: | They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors. |
| Outcome: | The proposed approach achieves superior performance compared with state-of-the-art approaches. |
An MRC Framework for Semantic Role Labeling (2022.coling-1)
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| Challenge: | Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles. |
| Approach: | They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding . |
| Outcome: | The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense . |
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)
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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
| Challenge: | Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification . |
| Approach: | They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity. |
| Outcome: | The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues. |
FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)
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| Challenge: | Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge. |
| Approach: | They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences. |
| Outcome: | The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences. |
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)
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| Challenge: | Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level . |
| Approach: | They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure. |
| Outcome: | The proposed approach improves performance by 3.4% on Squall. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection (2021.emnlp-main)
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| Challenge: | Existing studies studying OOD detection in NLP often rely on external data to diversify model predictions. |
| Approach: | They propose a framework which mimics OOD detection behavior without external data . they take text classification as an archetype and compare them to existing datasets . |
| Outcome: | The proposed framework can resolve in- and out-distribution examples in a natural way using existing datasets. |
BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)
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Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun
| Challenge: | Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks. |
| Approach: | They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost. |
| Outcome: | The proposed toolkit can support big model inference and tuning at extremely low computation cost. |
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder (2025.acl-long)
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| Challenge: | Existing embedding models excel at capturing general meaning, but overlook ideological nuances, limiting their effectiveness in political bias tasks. |
| Approach: | They propose a framework to Produce inteRpretable polItical biaS eMbeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models in political bias classification . the proposed framework offers highly interpretable representations for political analysis . |
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)
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| Challenge: | Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. |
| Approach: | They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability. |
| Outcome: | The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset. |
Do Influence Functions Work on Large Language Models? (2025.findings-emnlp)
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| Challenge: | Influence functions are important for quantifying the impact of individual training data points on a model’s predictions. |
| Approach: | They conduct a systematic study to address a key question: do influence functions work on large language models? |
| Outcome: | The influence functions perform poorly across multiple tasks and are therefore unsuitable for large language models. |
Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)
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| Challenge: | Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases. |
| Approach: | They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules . |
| Outcome: | The proposed framework is able to provide faithful explanations for legal case retrieval. |
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)
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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu
| Challenge: | Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks. |
| Approach: | They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability. |
| Outcome: | The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios. |
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)
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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu
| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks. |
| Approach: | They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. |
| Outcome: | The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks. |
Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing (2024.findings-emnlp)
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| Challenge: | Existing defense methods focus on detecting harmful prompts or reducing the likelihood of harmful responses. |
| Approach: | They propose a layer-specific editing method to align LLMs to harmful prompts by supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed method improves the performance of large language models against jailbreak attacks while maintaining performance on benign prompts. |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated. |
| Approach: | They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty. |
| Outcome: | The proposed framework improves performance and efficiency over multiple tasks over multiple datasets. |
Amanda: Adaptively Modality-Balanced Domain Adaptation for Multimodal Emotion Recognition (2024.findings-acl)
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| Challenge: | Emotion recognition is a multimodal learning method that can be used for data scarcity. |
| Approach: | They propose to use Adaptively modality-balanced domain adaptation to balance the alignment of different modalities for multimodal emotion recognition. |
| Outcome: | The proposed model outperforms competing models on common datasets on multimodal emotion recognition. |
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)
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Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen
| Challenge: | Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. |
| Approach: | They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. |
| Outcome: | Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods. |
Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models (2026.acl-long)
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| Challenge: | Despite their distinct external representations, a deeper analysis reveals their intrinsic nature: instructions serve as a natural language compression devised by humans for data governing specific mapping patterns, whereas parameters act as 'neuro compression' of the same task data. |
| Approach: | They propose a neural network framework to model and learn the bi-directional mappings between instructions and parameters of large language models by evaluating it on the tasks of instruction deduction and induction. |
| Outcome: | The proposed framework can map one of the instructions/parameters to the other by evaluating it on the tasks of instruction deduction and induction. |
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)
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| Challenge: | Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation . |
| Approach: | They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently . |
| Outcome: | The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets. |
Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025.findings-emnlp)
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| Challenge: | Emotional reasoning is essential for improving human-AI interactions, especially in mental health support and empathetic systems. |
| Approach: | They propose a third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisals, and Reappraisal. |
| Outcome: | The proposed model outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability. |
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
| Approach: | They propose a model that integrates symbolic data into LLM training without loss of generality ability. |
| Outcome: | The proposed model performs better on symbol- and NL-centric tasks. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features (C18-1)
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| Challenge: | Existing studies on learning social media content focus on single modal or bi-modal learning, but this approach is non-trivial and challenging because content is multi-modal and involves several types of data, including text, audio, and image. |
| Approach: | They propose to combine textual, acoustic, and visual information to learn social media content by fusing them jointly. |
| Outcome: | The proposed model outperforms the state-of-the-art approaches on real-world datasets by a large margin. |
ItD: Large Language Models Can Teach Themselves Induction through Deduction (2024.acl-long)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) have limited ability to conduct induction. |
| Approach: | They propose a framework to enable LLMs to teach themselves induction through deduction. |
| Outcome: | The proposed framework improves performance on two induction benchmarks and shows that it can be used to teach induction through deduction. |
DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations (2024.findings-emnlp)
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| Challenge: | Existing methods for Emotion Recognition in conversations are insufficient in understanding the rich historical emotional context. |
| Approach: | They propose a novel model that utilizes a "recall-detect-predict" framework to imitate human emotional reasoning by 'recalling' past interactions of a speaker to collect emotional cues. |
| Outcome: | The proposed model outperforms existing methods on three benchmark datasets and significantly outperformed existing methods. |
KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)
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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu
| Challenge: | Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) . |
| Approach: | They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). |
| Outcome: | The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. |
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
| Outcome: | The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language. |
Jailbreaking Multimodal Large Language Models using Multi-Clip Video (2026.acl-long)
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| Challenge: | Existing studies show that video inputs can bypass safety alignment, yet it remains unclear which properties of video input induce this vulnerability. |
| Approach: | They propose a simple image-based defense that mitigates the vulnerability of MLLMs by analyzing video inputs. |
| Outcome: | The proposed defense leverages the relative robustness of the image modality. |
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored. |
| Approach: | They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI. |
| Outcome: | The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score. |
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)
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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge. |
| Approach: | They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass. |
| Outcome: | The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively. |
Efficient Knowledge Infusion via KG-LLM Alignment (2024.findings-acl)
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| Challenge: | Existing methods for knowledge infusion face knowledge mismatch and poor information compliance of LLMs with knowledge graphs. |
| Approach: | They propose a three-stage alignment strategy to enhance the LLM's capability to utilize information from knowledge graphs. |
| Outcome: | The proposed method outperforms baselines on biomedical question-answering datasets and outperformed existing methods. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
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| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
| Approach: | They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories. |
| Outcome: | The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models. |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)
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| Challenge: | Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events. |
| Approach: | They propose a framework for event extraction which models the relations between arguments by an event matrix. |
| Outcome: | The proposed framework beats all the advanced competitors on 3 widely-used datasets. |
Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster. |
| Approach: | They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort. |
| Outcome: | The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)
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| Challenge: | Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus. |
| Approach: | They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses. |
| Outcome: | The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets. |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Effective In-Context Example Selection through Data Compression (2024.findings-acl)
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| Challenge: | In-context learning has been validated in large language models, but the mechanism and selection strategy for in-cont example selection lacks systematic and in-depth research. |
| Approach: | They propose a data compression approach to select in-context examples using large language models. |
| Outcome: | The proposed method shows a significant improvement of 5.90% across five real-world datasets using four language models. |
Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026.acl-long)
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Luoyang Sun, Guangyan Li, Cheng Deng, Haifeng Zhang, Jian Zhao, Yongqiang Tang, Wensheng Zhang, Jun Wang
| Challenge: | Large language models (LLMs) excel at natural language tasks but face deployment bottlenecks due to computational demands. |
| Approach: | They propose a training-free framework that exploits activation and weight sparsity . they use a three-tier routing strategy that uses magnitude-based pruning . |
| Outcome: | Experiments on Llama and Mistral models show that DAWS outperforms activation-weight sparsity pruning methods. |
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs (2026.findings-acl)
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| Challenge: | Personalization can inadvertently distort factual reasoning when faced with factual queries. |
| Approach: | They propose a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. |
| Outcome: | Experiments across multiple LLM backbones and personalization methods show that FPPS significantly improves factual accuracy while maintaining personalized performance. |
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database. |
| Approach: | They propose to use a knowledge database to enrich the input of LLMs by retrieving information from the relevant knowledge database. |
| Outcome: | The proposed approach can achieve 98% true positive rate while maintaining a false positive rate close to 1%. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
| Approach: | They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context. |
| Outcome: | The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs. |
Zero-Shot Defense Against Toxic Images via Inherent Multimodal Alignment in LVLMs (2025.findings-emnlp)
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| Challenge: | Existing safeguards relying on pre-filtering or fine-tuning are costly and diminish overall utility. |
| Approach: | They propose a lightweight method that leverages LVLMs’ inherent multimodal alignment for zero-shot toxic image detection. |
| Outcome: | The proposed method achieves a 66.9% defense success rate with only 3.2% false positive rate and 7.2% overhead. |
ChatUIE: Exploring Chat-based Unified Information Extraction Using Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have shown impressive performance in general chat, but their domain-specific capabilities have certain limitations. |
| Approach: | They propose a unified information extraction framework built upon ChatGLM that incorporates domain-specific modeling to extract structured information from natural language. |
| Outcome: | The proposed framework significantly improves the performance of information extraction tasks with a slight decrease in chatting ability. |