Papers by Rui Xia

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text.
Approach:	They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document.
Outcome:	The proposed task is based on a benchmark emotion cause corpus.

Copied to clipboard

Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)

Copied to clipboard

Jianfei Yu, Ziyan Li, Jieming Wang, Rui Xia

Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction.
Approach:	They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems .
Outcome:	The proposed framework outperforms baseline systems on the GMNER task.

Copied to clipboard

Fanfan Wang, Xiangqing Shen, Jianfei Yu, Rui Xia

Dialogue Systems & Conversational Agents

Challenge:	Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality.
Approach:	They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality.
Outcome:	The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy.

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)

Copied to clipboard

Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Syntactic Text Processing

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

MEMIT-Merge: Addressing MEMIT’s Key-Value Conflicts in Same-Subject Batch Editing for LLMs (2025.findings-acl)

Copied to clipboard

Zilu Dong, Xiangqing Shen, Rui Xia

Challenge:	Existing knowledge editing techniques that modify models’ internal knowledge without full model retraining have gained significant attention.
Approach:	They propose an enhanced approach that merges value computation processes for facts sharing the same subject to improve editing efficiency.
Outcome:	The proposed method maintains a 98% editing success rate on same-subject and distinct-sub subject datasets while the original success rate drops to 46%.

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026.acl-long)

Copied to clipboard

Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley

Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)

Challenge:	Existing Large Language Models exhibit critical vulnerability to indirect prompt injection attacks, where instructions injected within in the prompt context can override the user's intent.
Approach:	They propose a neural pruning algorithm that prunes neurons associated with instruction-following during KV cache encoding of the prompt context.
Outcome:	The proposed approach significantly reduces the attack success rate while preserving the model's ability to follow user instructions.

Copied to clipboard

Jianfei Yu, Chenggong Gong, Rui Xia

Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)

Challenge:	Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data.
Approach:	They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain.
Outcome:	The proposed approach is superior to state-of-the-art domain adaptation methods.

Copied to clipboard

Xiao Wang, Tianze Chen, Qiming Ge, Han Xia, Rong Bao, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

From Phrases to Subgraphs: Fine-Grained Semantic Parsing for Knowledge Graph Question Answering (2025.findings-acl)

Challenge:	Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks.
Approach:	They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially.
Outcome:	The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks.

Copied to clipboard

Yurun Song, Xiangqing Shen, Rui Xia

BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations (2023.emnlp-main)

Challenge:	Existing approaches to knowledge graph question answering (KGQA) face semantic misalignment and reasoning noise.
Approach:	They propose a fine-grained semantic parsing framework for KGQA that maps natural language queries to executable logical forms.
Outcome:	The proposed framework achieves 18.5% performance improvement over the SOTA on a multi-hop CWQ dataset.

Copied to clipboard

Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan

Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)

Challenge:	et al., 2022) argue that the current models for drug discovery lack the ability to integrate molecules, proteins, and natural language.
Approach:	They propose a framework that integrates biological knowledge with chemical knowledge and natural language associations.
Outcome:	The proposed framework shows superior performance across a wide range of tasks.

Copied to clipboard

Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui

Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning.
Approach:	STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics.
Outcome:	STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels.

Copied to clipboard

Yurun Song, Xiangqing Shen, Jianfei Yu, Rui Xia

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)

Challenge:	Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens.
Approach:	They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability.
Outcome:	The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations.

Copied to clipboard

Nan Song, Hongjie Cai, Rui Xia, Jianfei Yu, Zhen Wu, Xinyu Dai

Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths (2023.acl-long)

Challenge:	Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention.
Approach:	They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments.
Outcome:	The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task.

Copied to clipboard

Xiangqing Shen, Siwei Wu, Rui Xia

UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)

Challenge:	ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., head event, relation, tail event.
Approach:	They propose a CSKG completion method called Rel-CSKGC to predict the relation given the head event and tail event of a triplet and train a model based on existing triplets.
Outcome:	The proposed method is based on existing triplets and can be used to complete the missing links in ATOMIC.

Copied to clipboard

Zinong Yang, Feng Xu, Jianfei Yu, Rui Xia

ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)

Challenge:	Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages.
Approach:	They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence.
Outcome:	The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments.

Copied to clipboard

Zixiang Ding, Rui Xia, Jianfei Yu

Semantic Role Labeling with Heterogeneous Syntactic Knowledge (2020.coling-main)

Challenge:	a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs .
Approach:	They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme.
Outcome:	The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws .

Copied to clipboard

Qingrong Xia, Rui Wang, Zhenghua Li, Yue Zhang, Min Zhang

Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method (2024.findings-acl)

Challenge:	Recent work on incorporating syntactic knowledge into neural semantic role labeling has gained much attention . incorporating heterogeneous syntaktic knowledge brings significant improvements over strong baselines .
Approach:	They propose to encode heterogeneous syntactic knowledge for SRL from explicit and implicit representations from heterogenous treebanks.
Outcome:	The proposed approaches improve on two widely-used benchmark datasets.

Copied to clipboard

Tian Xia, Zhiwei He, Tong Ren, Yibo Miao, Zhuosheng Zhang, Yang Yang, Rui Wang

Challenge:	Using a novel approach, we can evaluate an agent’s bargaining abilities as an asymmetric incomplete information game.
Approach:	They propose an approach that integrates a deterministic Offer Generator and an LLM Narrator to create natural language sentences for generated offers.
Outcome:	The proposed approach improves the buyer’s deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned.

VCD: A Dataset for Visual Commonsense Discovery in Images (2025.findings-acl)

Copied to clipboard

Xiangqing Shen, Fanfan Wang, Siwei Wu, Rui Xia

Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering (2023.findings-acl)

Challenge:	Visual commonsense data sets lack visual grounded representations of commonsensense . existing knowledge bases lack visual-based knowledge tied to actual visual scenes .
Approach:	They present a large-scale visual commonsense dataset with over 100,000 images and 14 million object-commonsense pairs that integrates both Seen (directly observable) and Unseen (inferrable) commonsens.
Outcome:	The proposed model integrates Seen (directly observable) and Unseen (inferrable) commonsense across Property, Action, and Space aspects.

Copied to clipboard

Siwei Wu, Xiangqing Shen, Rui Xia

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

Challenge:	Commonsense knowledge graphs are typically represented by short text, resulting in many different nodes representing the same concept.
Approach:	They propose a framework based on Contrastive Pretraining and Node Clustering to solve these problems.
Outcome:	The proposed framework outperforms state-of-the-art methods on two CSKG completion benchmarks.

Copied to clipboard

Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li

Challenge:	Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach:	They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome:	The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.

Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)

Copied to clipboard

Hongjie Cai, Yaofeng Tu, Xiangsheng Zhou, Jianfei Yu, Rui Xia

Emotion Cause Extraction on Social Media without Human Annotation (2023.findings-acl)

Challenge:	Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text.
Approach:	They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis.
Outcome:	The proposed model can consistently achieve the best results on four benchmarks.

Copied to clipboard

Debin Xiao, Rui Xia, Jianfei Yu

Low-Resource NLP

Challenge:	Existing studies have focused on extracting emotion causes from news articles, but lack of fine-grained annotations has limited the ECE task.
Approach:	They propose a new ECE framework that extracts emotion causes from social media data without relying on human annotations.
Outcome:	The proposed framework achieves high extraction performance and generalizability without relying on human annotations.

Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)

Copied to clipboard

Junjie Li, Jianfei Yu, Rui Xia

Low-Resource NLP

Challenge:	Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations.
Approach:	They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain.
Outcome:	The proposed framework is more effective than previous domain adaptation methods on three datasets.

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)

Copied to clipboard

Wenjie Zheng, Jianfei Yu, Rui Xia, Shijin Wang

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)

Challenge:	Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes.
Approach:	They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence.
Outcome:	The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset.

Copied to clipboard

Hao Chen, Rui Xia, Jianfei Yu

Information Retrieval

Challenge:	Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem.
Approach:	They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification.
Outcome:	The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets.

Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization (2026.acl-srw)

Copied to clipboard

Shiyan Liu, Qifeng Xia, Qiyun Xia, Yisheng Liu, Xinyu Yu, Rui Qu

Stacked AMR Parsing with Silver Data (2021.findings-emnlp)

Challenge:	Automatic prompt optimization (APO) is a powerful paradigm for improving LLM performance without manual prompt engineering.
Approach:	They propose a framework that decouples hypothesis generation from prompt rewriting . they propose VISTA framework that recovers accuracy to 87.57% on same defective seed .
Outcome:	The proposed framework outperforms baselines on GSM8K and AIME2025 on a defective seed.

Copied to clipboard

Qingrong Xia, Zhenghua Li, Rui Wang, Min Zhang

A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020.emnlp-main)

Challenge:	Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing.
Approach:	They propose to use silver data to train a pre-trained abstract meaning representation model.
Outcome:	The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model.

Copied to clipboard

Rui Xia, Kaizhou Xuan, Jianfei Yu

Challenge:	Existing methods to rumor detection ignored dynamical evolution of an event and failed to capture its unique features in different states.
Approach:	They propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation.
Outcome:	The proposed framework can significantly improve the rumor detection accuracy in comparison with some strong baseline systems.

End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020.emnlp-main)

Copied to clipboard

Zixiang Ding, Rui Xia, Jianfei Yu

Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)

Challenge:	Existing methods to extract potential pairs of emotions ignore the fact that the cause and the emotion it triggers are inseparable.
Approach:	They propose two frameworks that combine multi-label learning and multi-labeled learning to extract emotion clauses . they evaluate a benchmark emotion cause corpus and find the best performance .
Outcome:	The proposed frameworks achieve the best performance among all compared systems on the ECPE task.

Copied to clipboard

Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia

Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge (2023.findings-emnlp)

Challenge:	Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies .
Approach:	They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV.
Outcome:	The proposed model outperforms existing methods on two benchmark datasets.

Copied to clipboard

Fanfan Wang, Jianfei Yu, Rui Xia

Reasoning

Challenge:	Existing studies on ECTEC focus on Causal Emotion Entailment and Emotion-Cause Pair Extraction in Conversations.
Approach:	They propose to decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner.
Outcome:	The proposed model outperforms competing systems on two benchmark datasets.

A Survey on In-context Learning (2024.emnlp-main)

Copied to clipboard

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)

Copied to clipboard

Hongjie Cai, Heqing Ma, Jianfei Yu, Rui Xia

Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)

Challenge:	Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities.
Approach:	They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT.
Outcome:	The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets.

Copied to clipboard

Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao

Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment (2024.acl-long)

Challenge:	Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model.
Approach:	They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs.
Outcome:	Experiments show that the proposed approach performs better than previous approaches on various benchmarks.

Copied to clipboard

Qiming Xie, Zengzhi Wang, Yi Feng, Rui Xia

Comparative Opinion Quintuple Extraction from Product Reviews (2021.emnlp-main)

Challenge:	Existing large language models often waver in their judgments when faced with follow-up questions . this is a challenge for generating reliable responses and building user trust .
Approach:	They propose a Follow-up Questioning Mechanism and two metrics to quantify this inconsistency . they also develop a framework that teaches large language models to maintain original correct judgments .
Outcome:	The proposed framework improves the general capabilities of large language models by allowing them to maintain original correct judgments.

Copied to clipboard

Ziheng Liu, Rui Xia, Jianfei Yu

From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)

Challenge:	Comparative opinion mining is an important task in opinion mining.
Approach:	They propose a task to extract comparative opinion quintuples from product reviews . they propose supplementary annotations and construct three datasets for the task .
Outcome:	The proposed method outperforms baseline systems on three datasets and represents a strong benchmark for COQE.

Copied to clipboard

Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, Ranran Zhen

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

Challenge:	Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning.
Approach:	They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs.
Outcome:	Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains.

Copied to clipboard

Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu

Towards Harmonized Uncertainty Estimation for Large Language Models (2025.acl-long)

Challenge:	distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction.
Approach:	They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections.
Outcome:	The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets.

Copied to clipboard

Rui Li, Jing Long, Muge Qi, Heming Xia, Lei Sha, Peiyi Wang, Zhifang Sui

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated exceptional capabilities in handling a wide range of downstream tasks.
Approach:	They propose a method that employs a lightweight model trained on data aligned with the target LLM’s performance to adjust uncertainty scores.
Outcome:	The proposed method achieves improvements of up to 60% over existing methods.

Copied to clipboard

Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia

LoReFact: Bridging the Logic Gap in Fact-Checking (2026.findings-acl)

Challenge:	Existing knowledge editing methods for large language models struggle to maintain logical consistency when propagating ripple effects to associated facts.
Approach:	They propose a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates.
Outcome:	The proposed framework improves logical generalization and specificity while maintaining reliability and specificness.

Copied to clipboard

Qiming Xie, Wenjie Zheng, Xiangqing Shen, Rui Xia

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Existing fact-checking methods focus on verification of individual facts, overlooking logical dependencies . a recent study shows that text containing logical errors may still be misjudged as factual .
Approach:	They propose a content–logic coupled factuality evaluation paradigm that conceptualizes factual dimension along two complementary dimensions: content factualism and logic factuity.
Outcome:	The proposed paradigm bridges the gap between factual verification and content factuality . it incorporates the logical dimension and a logic-aware metric to expose and penalize logical fallacies.

Copied to clipboard

Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

Copied to clipboard

Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

Challenge:	Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach:	They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome:	The proposed approach achieves state-of-the-art on two benchmark datasets.

Copied to clipboard

Chenggong Gong, Jianfei Yu, Rui Xia

Merlin’s Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting (2026.acl-long)

Challenge:	Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach:	They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome:	The proposed method improves on the state-of-the-art in two aspects of the ABSA task.

Copied to clipboard

Heming Xia, Cunxiao Du, Rui Li, Chak Tou Leong, Yongqi Li, Wenjie Li

How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025.findings-acl)

Challenge:	Large reasoning models (LRMs) have demonstrated proficiency in tackling complex tasks through step-by-step thinking.
Approach:	They propose a black-box persuasive prompting framework that generates concise responses without compromising accuracy.
Outcome:	The proposed framework reduces token usage while preserving performance.

Copied to clipboard

Rui Li, Heming Xia, Xinfeng Yuan, Qingxiu Dong, Lei Sha, Wenjie Li, Zhifang Sui

Challenge:	Recent studies have focused on dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins.
Approach:	They propose to integrate persona metadata into LLMs and use it to iteratively infer contextually appropriate behaviors within dynamic scenarios.
Outcome:	The proposed model is based on 15,846 distinct behaviors across 1,001 unique personas and incorporates persona metadata to iteratively infer appropriate behaviors within dynamic scenarios.

Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)

Copied to clipboard

Jianfei Yu, Qiankun Zhao, Rui Xia

HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)

Challenge:	Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain.
Approach:	They propose a domain-adaptive language model to generate labeled data from a source domain.
Outcome:	The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks.

Copied to clipboard

Jingyuan Ma, Rui Li, Zheng Li, Junfeng Liu, Heming Xia, Lei Sha, Zhifang Sui

Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)

Challenge:	Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities.
Approach:	They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% .
Outcome:	The proposed framework improves reasoning models by 13 percentage points over baseline.

Copied to clipboard

Yan Ling, Jianfei Yu, Rui Xia

ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)

Challenge:	Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models.
Approach:	They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks.

Copied to clipboard

Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu

Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise .
Approach:	They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following.
Outcome:	The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches.

Copied to clipboard

Hongjie Cai, Rui Xia, Jianfei Yu