Papers by Xing Xu

Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)

Challenge:	Large Language Models (LLMs) have recently achieved remarkable progress on complex reasoning tasks by leveraging extended Chain-of-Thought (CoT) techniques.
Approach:	They propose a method that uses Extended Chain-of-Thought (EFT) to reduce the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning.
Outcome:	The proposed method reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning.

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Mao Zhang, Yongxin Zhu, Zhen Liu, Zhimin Bao, Yunfei Wu, Xing Sun, Linli Xu

Sentiment Analysis

Challenge:	Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions.
Approach:	They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level.
Outcome:	The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency .

Red-Teaming LLM Multi-Agent Systems via Communication Attacks (2025.findings-acl)

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Pengfei He, Yuping Lin, Shen Dong, Han Xu, Yue Xing, Hui Liu

SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)

Challenge:	Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling agent collaboration through message-based communications.
Approach:	They propose an attack that exploits communication mechanisms in Large Language Model-based Multi-Agent Systems (LLM-MAS) by intercepting and manipulating inter-agent messages.
Outcome:	The proposed attack exploits communication mechanisms in large language model-based multi-agent systems by intercepting and manipulating inter-agencies.

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Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Gao Xing, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang

Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models (2026.acl-long)

Challenge:	Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence.
Approach:	They propose a benchmark to evaluate the sociality of role-playing agents using LLMs.
Outcome:	The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances.

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Yu Tian, Jie Xing, Ziming Li, Jiang Li, Zehua Duo, Tian Lan, Xu Liu, Guanglai Gao, Xiangdong Su

Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models (2025.coling-main)

Challenge:	Existing studies suggest that failures of large language models in social contexts are not due to limited linguistic competence, but to inappropriate recognition.
Approach:	They propose a framework that decomposes social adaptation into three orthogonal dimensions and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions.
Outcome:	The proposed framework decomposes social adaptation into three orthogonal dimensions and conducts controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions.

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Jiahui Li, Yongchang Hao, Haoyu Xu, Xing Wang, Yu Hong

PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

Challenge:	Despite advances in training Large Language Models, they remain vulnerable to jailbreak, an adversarial attack method.
Approach:	They propose an adversarial jailbreak algorithm that exploits the gradient information of the suffix tokens to accelerate the optimization process.
Outcome:	The proposed model achieves 1.5x speedup while maintaining high attack success rates.

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Yunxiao Shi, Wujiang Xu, Zhang Zeqi, Xing Zi, Qiang Wu, Min Xu

Challenge:	Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
Approach:	They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling .
Outcome:	The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data.

PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling (2025.acl-long)

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Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, Xiangmin Xu

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

Challenge:	Existing mental health LLMs do not consider the fact that different psychological counselors exhibit different personal styles.
Approach:	They propose a framework that uses LLMs to construct the digital twin of psychological counselor with personalized counseling style.
Outcome:	The proposed framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to baselines.

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

Reasoning

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

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)

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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy.
Approach:	They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction.
Outcome:	The proposed model improves generalization ability and makes distant link prediction significantly easier.

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

Multimodality

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)

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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

Challenge:	Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society.
Approach:	They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system .
Outcome:	The proposed system simulates trending topics under poisoning attacks on social media platforms.

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation (2022.coling-1)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Xiaolin Xing, Yu Hong, Minhan Xu, Jianmin Yao, Guodong Zhou

Multilinguality

Challenge:	Existing approaches to train NMT models rely on sparse parallel data . a variety of PC variants yield significant improvements for low-resource NMT .
Approach:	They propose to transfer well-trained NMT models to low-resource languages by bidirectionally-adaptive learning strategy . they divide inner constituents of Parent encoder into two "teams" aiming to adapt to characteristics of low- and high-resourced languages .
Outcome:	The proposed method improves on low-resource NMT models with a variety of PC variants.

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)

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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee

Answering Narrative-Driven Recommendation Queries via a Retrieve–Rank Paradigm and the OCG-Agent (2025.emnlp-main)

Challenge:	Large language models (LLMs) have shown unprecedented performance across various tasks.
Approach:	They propose an easy-to-use framework that integrates adapters into LLMs . they evaluate adapters on 14 datasets from two different reasoning tasks .
Outcome:	The proposed framework can be used to fine-tune open-access language models with task-specific data and instruction data.

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Yunxiao Shi, Haoning Shang, Xing Zi, Wujiang Xu, Yue Feng, Min Xu

Question Answering

Challenge:	Existing approaches to generate narrative-driven recommendation are based on large language models (LLMs) but the RAG paradigm is inherently ill-suited for such special queries.
Approach:	They propose a novel retrieve-rank paradigm that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information.
Outcome:	The proposed paradigm outperforms the existing paradigm and the existing one under real-world scenarios.

Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)

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Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie

Exploring Memorization in Fine-tuned Language Models (2024.acl-long)

Challenge:	Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation.
Approach:	They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models.
Outcome:	The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility.

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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin

From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)

Challenge:	Existing studies have shown that pre-trained langauge models tend to memorize and regenerate segments of their pre-training corpus when prompted appropriately.
Approach:	They conduct the first comprehensive analysis to explore language models’ memorization during fine-tuning across tasks.
Outcome:	The proposed analysis shows that memorization presents a strong disparity among different fine-tuning tasks.

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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu

Low-Resource NLP

Challenge:	Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning.
Approach:	They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model.
Outcome:	The proposed method improves egocentric reasoning abilities on six tasks.

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

Data Poisoning for In-context Learning (2025.findings-naacl)

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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Pengfei He, Han Xu, Yue Xing, Hui Liu, Makoto Yamada, Jiliang Tang

TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)

Challenge:	In-context learning (ICL) has emerged as a capability of large language models (LLMs) but there is limited understanding of its vulnerability against data poisoning attacks.
Approach:	They propose an attack method that exploits ICL’s unique learning mechanisms by identifying discrete text perturbations that influence LLM hidden states.
Outcome:	The proposed attack method exploits ICL’s learning mechanisms by identifying discrete text perturbations that influence LLM hidden states.

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Bei Li, Yi Jing, Xu Tan, Zhen Xing, Tong Xiao, Jingbo Zhu

TailorRPA: A Retrieval-Based Framework for Eliciting Personalized and Coherent Role-Playing Agents in General Domain (2025.findings-emnlp)

Challenge:	Prior work has focused on treating subwords as basic units in developing such systems.
Approach:	They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences.
Outcome:	The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks.

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Zhenpeng Gao, Xiaofen Xing, Xiangmin Xu

Reasoning

Challenge:	a recent study has shown that general domain oriented role-playing agents can maintain character properties in a wide range of tasks beyond scenario based chit-chatting.
Approach:	They propose a retrieval-based framework to harvest tailored general domain instructions . they use general-domain protective queries to shape character-wise knowledge boundary .
Outcome:	The proposed framework improves integration of fine-grained memories and protects character knowledge boundary . it also improves character hallucination in general domain, compared to baseline methods .

FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability (2024.acl-long)

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Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, Ran Xu, Wenpeng Yin, Caiming Xiong

Challenge:	Existing benchmarks fail to assess large language models’ format-following proficiency adequately.
Approach:	They propose a benchmark to evaluate large language models' ability to follow complex, domain-specific formats.
Outcome:	The proposed framework evaluates large language models' ability to follow complex, domain-specific formats across open-source and closed-source models.

DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization (2025.findings-naacl)

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Pucheng Dang, Xing Hu, Dong Li, Rui Zhang, Qi Guo, Kaidi Xu

Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement (2026.acl-long)

Challenge:	Existing text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images.
Approach:	They propose a method which uses zeroth order optimization to procure gradient approximations and harnesses both C-PRV and D-PRv to enhance attack prompts within a discrete prompt space.
Outcome:	The proposed method achieves an 8.5% higher average attack success rate than previous works on multiple state-of-the-art safety mechanisms.

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Zexu Sun, Yongcheng Zeng, Erxue Min, Heyang Gao, Bokai Ji, Dugang Liu, Xing Tang, Xiuqiang He, Xu Chen

TreeRAG: Unleashing the Power of Hierarchical Storage for Enhanced Knowledge Retrieval in Long Documents (2025.findings-acl)

Challenge:	Recent advances in Large Language Models have demonstrated notable inferential capacities via reinforcement learning (RL) however, “zero-RL” approaches relying on fixed prompt templates introduce substantial sampling inefficiencies for weak LLMs.
Approach:	They propose a hierarchical metacognitive RL framework that decomposes zero-accuracy problems into subproblems and prompts the policy to refine answers by referencing previous wrong solutions.
Outcome:	The proposed framework improves sample utilization and sample efficiency and accelerates convergence compared to baselines.

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Wenyu Tao, Xiaofen Xing, Yirong Chen, Linyi Huang, Xiangmin Xu

Information Retrieval

Challenge:	Traditional RAG frameworks struggle to retrieve all relevant knowledge points . a new approach to retrieve long documents is proposed to improve performance in NLP .
Approach:	They propose a tree-based approach to document knowledge retrieval that preserves hierarchical structure . treeRAG is a key technique for enhancing the text generation capabilities of Large Language Models .
Outcome:	The proposed approach improves recall quality and precision compared to existing methods and better performance to question-answering tasks.

Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation (N19-1)

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Xing Niu, Weijia Xu, Marine Carpuat

Multilinguality

Challenge:	Existing work has addressed this problem by leveraging monolingual or multilingual data.
Approach:	They propose to introduce a differentiable reconstruction loss for neural machine translation to exploit the limited amounts of parallel text available in low-resource settings.
Outcome:	The proposed approach achieves small but consistent BLEU improvements on four language pairs in both translation directions and outperforms an alternative differentiable reconstruction strategy based on hidden states.

Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)

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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li

PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents (2026.eacl-industry)

Challenge:	Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student.
Approach:	They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions.
Outcome:	The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures.

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Minjia Wang, Yunfeng Wang, Xiao Ma, Dexin Lv, Qifan Guo, Lynn Zheng, Benliang Wang, Lei Wang, Jiannan Li, Yongwei Xing, Junzhe Xu, Zheng Sun

Challenge:	Publicly available corpora cover only slivers of human activity, such as email threads, chat logs, purchase histories, sensor traces, and provide large-scale supervision for data-hungry machine-learning pipelines.
Approach:	They propose a method for synthesizing realistic digital footprints using large language model agents from a structured user profile.
Outcome:	The proposed method generates diverse sequences of user events, producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc.

CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)

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Mingyu Chen, Jingkai Lin, Zhaojie Chu, Xiaofen Xing, Yirong Chen, Xiangmin Xu

DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (2022.coling-1)

Challenge:	Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response.
Approach:	They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues.
Outcome:	The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling.

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Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, Caiming Xiong

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	Existing methods that only address a fixed set of fields are difficult to use for different form types.
Approach:	They propose a value retrieval method with arbitrary queries for form-like documents . they propose 'docQueryNet' to predict target value based on understanding of layout and semantics of a form .
Outcome:	The proposed method outperforms existing methods on value retrieval . it improves document understanding on large-scale model pre-training by 17% .

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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

Momoka-RAG: MCTS-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation (2026.findings-acl)

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

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Wenyu Tao, Xiaofen Xing, Zeliang Li, Xiangmin Xu

Information Retrieval

Challenge:	Existing frameworks that rely on fixed-length chunking are unsuitable for long-document tasks due to their passive and mechanical approach to knowledge structure.
Approach:	They propose a framework that utilizes Monte Carlo Tree Search to proactively uncover connections among chunks and construct optimal semantic information paths with the objective of completing semantic relationships.
Outcome:	The proposed framework achieves higher precision while maintaining competitive recall compared to other RAG frameworks.

Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation (N19-1)

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Weijia Xu, Xing Niu, Marine Carpuat

Machine Translation

Challenge:	Existing approaches to correct exposure bias in machine translation are inadequate . scheduled sampling assumes that words are aligned at each time step .
Approach:	They propose a differentiable sampling algorithm that optimizes the probability that the reference can be aligned with the sampled output.
Outcome:	The proposed approach improves BLEU on translation tasks and is simpler to train with no sampling schedule.

EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint (2025.emnlp-main)

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Zhenhua Xu, Meng Han, Wenpeng Xing

Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis (2024.emnlp-main)

Challenge:	Existing fingerprinting methods require impractical white-box access or introduce detectable statistical anomalies.
Approach:	They propose a gray-box fingerprinting framework that ensures stealthy and robust model provenance tracing.
Outcome:	The proposed framework is the first to repurpose Membership Inference Attacks (MIAs) for defensive use, embedding ownership signals via memorization instead of artificial trigger-output overfitting.

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Yuping Lin, Pengfei He, Han Xu, Yue Xing, Makoto Yamada, Hui Liu, Jiliang Tang

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are susceptible to a type of attack known as jailbreaking, which misleads LLMs to output harmful contents.
Approach:	They propose to leverage hidden representations into existing jailbreak targets to move the attacks along the acceptance direction.
Outcome:	The proposed methods are validated using the objective of existing jailbreak attacks.

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Xubin Yue, Zhenhua Xu, Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han

QuantAgents: Towards Multi-agent Financial System via Simulated Trading (2025.findings-emnlp)

Challenge:	Existing black-box fingerprinting techniques rely on overfitting high-perplexity trigger patterns . experimental results show that model editing in the fingerprint domain exhibits unique advantages .
Approach:	They propose a prefix-enhanced fingerprint editing framework that encodes copyright information into parameter offsets through dual-channel knowledge edit to achieve covert embedding of fingerprint features.
Outcome:	The proposed model editing framework achieves 90% trigger precision in mainstream architectures . the proposed model editor achieves the 90% accuracy in mainstream models .

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Xiangyu Li, Yawen Zeng, Xiaofen Xing, Jin Xu, Xiangmin Xu

Challenge:	Existing LLM-based agent models exhibit significant deviations from real-world fund companies.
Approach:	They propose a multi-agent financial system that incorporates simulated trading . they propose simulated trades are evaluated without assuming actual risks .
Outcome:	The proposed system evaluates various investment strategies without assuming actual risks without involving real-world investors.

On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

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

Challenge:	under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach:	They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome:	The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.

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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

Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation (2020.findings-emnlp)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Weijia Xu, Xing Niu, Marine Carpuat

Text Generation

Challenge:	Iterative Back-Translation and Dual Learning use different objectives and heuristic gradient approximation strategies, and have not been extensively compared.
Approach:	They propose a dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning.
Outcome:	The proposed method is more effective than Dual Learning on German-English and Turkish-English tasks.

Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)

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Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong

Multimodality

Challenge:	Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias .
Approach:	They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a .
Outcome:	Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets.

SAKI-RAG: Mitigating Context Fragmentation in Long-Document RAG via Sentence-level Attention Knowledge Integration (2025.emnlp-main)

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Wenyu Tao, Xiaofen Xing, Zeliang Li, Xiangmin Xu

Chunking

Challenge:	Traditional Retrieval-Augmented Generation (RAG) frameworks segment documents into larger chunks to preserve contextual coherence . however, such chunking methods lead to fragmented contexts, isolated chunk semantics, and broken inter-chunk relationships .
Approach:	They propose a framework that maintains granular chunks while recovering their intrinsic semantic connections.
Outcome:	The proposed framework achieves better recall and precision compared to other RAG frameworks in long-document retrieval scenarios.

Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models (2025.acl-long)

Challenge:	Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data.
Approach:	They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data.
Outcome:	The proposed approach preserves key contextual information from the original data while reducing privacy risks.

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Jingxuan Zhang, Zhenhua Xu, Rui Hu, Wenpeng Xing, Xuhong Zhang, Meng Han

Data-to-Text Generation with Style Imitation (2020.findings-emnlp)

Challenge:	Large Language Models (LLMs) have raised critical concerns about model ownership and intellectual property protection.
Approach:	They propose a method for effectively removing backdoor-based fingerprints from LLMs . they propose deleting backdoor fingerprints using a transferable erasure mechanism .
Outcome:	The proposed method removes backdoor-based fingerprints while maintaining model performance.

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Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

Text Generation

Challenge:	Recent approaches to data-to-text generation focus on improving content fidelity, but lack explicit control over writing styles.
Approach:	They propose a way to control writing styles by using existing sentences as "soft" templates . they conduct experiments in restaurants and sports domains to test their approach .
Outcome:	The proposed approach achieves stronger performance than a range of comparison methods.

Unsupervised Sign Language Translation and Generation (2024.findings-acl)

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Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang

Multilinguality

Challenge:	Experimental results on the BBC-Oxford Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models.
Approach:	They propose an unsupervised sign language translation and generation network that learns from abundant single-modality data without parallel sign language data.
Outcome:	The proposed model achieves competitive results compared to baseline models on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language data.

Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG (2026.acl-long)

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Chuang Zhou, Zheng Yuan, Linhao Luo, Zhaozhuo Xu, Yilin Xiao, Junnan Dong, Siyu An, di Yin, Xing Sun, Xiao Huang

Query-Aware Knowledge Retrieval via Hyperbolic Structuring (2026.acl-long)

Challenge:	Retrieval-augmented generation (RAG) has been used for enhancing large language models with external knowledge.
Approach:	They propose a framework for mining efficient graph structures via hashing to enhance RAG . they adopt an inductive paradigm where global graph structure emerges from local hash collisions .
Outcome:	The proposed framework outperforms existing baselines while requiring no GPU resources or token budget.

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Chuang Zhou, Junnan Dong, Yilin Xiao, Shengyuan Chen, Su Dong, di Yin, Xing Sun, Zhaozhuo Xu, Xiao Huang

Multimodality

Challenge:	Existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relationships.
Approach:	They propose a query-centric retrieval framework that explicitly integrates structured knowledge graphs to support complex reasoning tasks.
Outcome:	Extensive experiments on three benchmark datasets show that HyperRAG outperforms baselines.

SoulChat: Improving LLMs’ Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations (2023.findings-emnlp)

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Yirong Chen, Xiaofen Xing, Jingkai Lin, Huimin Zheng, Zhenyu Wang, Qi Liu, Xiangmin Xu

Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)

Challenge:	Large language models (LLMs) are used in psychological counseling to provide universal advice.
Approach:	They constructed a multi-turn empathetic conversation dataset with 2 million samples . they found that the model's empathy ability is enhanced when finetuning .
Outcome:	Experiments show that large language models can be finetuned to provide empathy . but, when applied to mental health or emotional support conversation, there are three main issues .

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Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

Towards Better Value Principles for Large Language Model Alignment: A Systematic Evaluation and Enhancement (2025.acl-long)

Challenge:	Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions.
Approach:	They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph.
Outcome:	The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm.

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Bingbing Xu, Jing Yao, Xiaoyuan Yi, Aishan Maoliniyazi, Xing Xie, Xiaofeng Meng