Papers by Zheng Xie

CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models (2024.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge and improve factual accuracy.
Approach:	They propose a framework that integrates neuro-symbolic verification with reinforcement learning to optimize logical consistency.
Outcome:	The proposed framework outperforms strong RAG baselines on hotpotQA, ASQA, and TriviaQA.

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Qian Lou, Xin Liang, Jiaqi Xue, Yancheng Zhang, Rui Xie, Mengxin Zheng

Bridging Distance and Spectral Positional Encodings via Anchor-Based Diffusion Geometry Approximation (2026.acl-long)

Challenge:	Existing certified robustness methods for certifying input-specific text perturbations have shown promise in certifyling UTPs, but masking only adversarial words can eliminate the attack.
Approach:	They propose a method to certify a language model’s robustness against UTPs by using random smoothing.
Outcome:	The proposed method achieves high certified accuracy under extensive masking and achieves state-of-the-art results in multiple settings.

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Yan Zimo, Zheng Xie, Runfan Duan, Chang Liu, Wumei Du

Structured Data in NLP

Challenge:	Molecular graph learning benefits from positional signals that capture local neighborhoods and global topology.
Approach:	They propose to use anchor-based distance encodings to approximate diffusion geometry.
Outcome:	The proposed model outperforms models without positional encodings on DrugBank with a shared GNP-based DDI backbone.

Few-shot Classification with Hypersphere Modeling of Prototypes (2023.findings-acl)

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Ning Ding, Yulin Chen, Ganqu Cui, Xiaobin Wang, Haitao Zheng, Zhiyuan Liu, Pengjun Xie

Automated Fine-Grained Mixture-of-Experts Quantization (2025.findings-acl)

Challenge:	Existing methods for fewshot learning use embeddings in space, but they lack expressivity and are difficult to perform statistically.
Approach:	They propose a method where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere’s center and the radius.
Outcome:	The proposed method is much more expressive than embeddings and performs better than statistical modeling.

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Zhanhao Xie, Yuexiao Ma, Xiawu Zheng, Fei Chao, Wanchen Sui, Yong Li, Shen Li, Rongrong Ji

Green & Sustainable NLP

Challenge:	specialized quantization framework for Mixture of Experts architectures is inadequate for model compression.
Approach:	They propose a specialized quantization framework for Mixture of Experts architectures . they find that expert networks exhibit distinctive channel-wise outlier distributions ."
Outcome:	The proposed framework improves on the Mixtral-8x7b-v0.1 architecture while maintaining minimal computational overhead.

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)

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Chengbing Wang, Wuqiang Zheng, Yang Zhang, Fengbin Zhu, Junyi Cheng, Yi Xie, Wenjie Wang, Fuli Feng

Language Models

Challenge:	Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy.
Approach:	They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality .
Outcome:	a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset.

Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)

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Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Haitao Zheng, Zhiyuan Liu, Pengjun Xie

Language Models

Challenge:	Existing approaches to optimize pre-trained language models are expensive and slow to scale.
Approach:	They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM .
Outcome:	The proposed method can achieve comparable results with other gradient-free and optimization-free baselines.

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)

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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Approach:	They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions.
Outcome:	The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks.

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Yongsen Zheng, Zongxuan Xie, Guohua Wang, Ziyao Liu, Liang Lin, Kwok-Yan Lam

Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)

Challenge:	Unfairness is a well-known challenge in Recommender Systems (RSs) some approaches have started to improve fairness in offline or static contexts, but it often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers.
Approach:	They propose a framework to promote multi-interest diversity fairness in RSs by establishing diverse hypergraphs through contrastive learning.
Outcome:	The proposed framework achieves state-of-the-art performance while effectively alleviating unfairness in two CRS-based datasets.

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Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, Zhiyuan Liu

Low-Resource NLP

Challenge:	Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded.
Approach:	They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models .
Outcome:	The proposed model is the first few-shot NER dataset and the largest human-crafted NER data set.

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

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Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan, Guotong Xie

SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)

Challenge:	Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations.
Approach:	They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention.
Outcome:	The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets.

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Hanghai Hong, Yibo Xie, Jiawei Zheng, Xiaoli Wang

Machine Translation

Challenge:	Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors.
Approach:	They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations.
Outcome:	The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations.

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

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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

PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)

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 .

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Zihan Zheng, Tianle Cui, Chuwen Xie, Jiahui Pan, Qianglong Chen, Lewei He

Reasoning

Challenge:	Recent studies have shown that LLMs can be significantly improved by integrating external tools.
Approach:	They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans.
Outcome:	The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates.

COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)

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Chengyue Jiang, Yong Jiang, Weiqi Wu, Yuting Zheng, Pengjun Xie, Kewei Tu

emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)

Challenge:	Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases.
Approach:	They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction.
Outcome:	The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase.

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Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, ShiLiang Zhang, Xie Chen

GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)

Challenge:	Existing models for speech emotion recognition are not suitable for emotional tasks.
Approach:	They propose a universal speech emotion representation model that is pre-trained on open-source emotion data.
Outcome:	euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets .

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Qizhuo Xie, Yunhui Liu, Yu Xing, Qianzi Hou, Xudong Jin, Tao Zheng, Tieke He

Neural News Recommendation with Long- and Short-term User Representations (P19-1)

Challenge:	Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete.
Approach:	They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook .
Outcome:	The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain.

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Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)

Challenge:	Existing news recommendation methods learn a single representation for each user, which may be insufficient.
Approach:	They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder.
Outcome:	The proposed approach can learn both long- and short-term user representations on a real-world dataset.

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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search (2021.findings-emnlp)

Challenge:	Existing solutions for visual document understanding lack granularity of document textlines.
Approach:	They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts.
Outcome:	The proposed system performs better on various VDU tasks in English and Chinese.

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Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, Zengxuan Wen

Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization (2024.lrec-main)

Challenge:	Existing relevance models rely on query-keyword pairs but keywords are usually short texts with scarce semantic information, which may not accurately reflect the underlying advertising purposes.
Approach:	They propose a bidding-graph augmented triple-based relevance model with three towers to deeply fuse the bidding graphs and semantic textual data.
Outcome:	The proposed model outperforms existing models on a large industry dataset and consistently outperformed existing models.

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Linzhi Wu, Xingyu Zhang, Yakun Zhang, Changyan Zheng, Tiejun Liu, Liang Xie, Ye Yan, Erwei Yin

Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)

Challenge:	Lip reading is a process of interpreting silent speech from visual lip movements . but lip reading in cross-speaker scenarios poses a challenging problem due to inter-speech variability .
Approach:	They propose to exploit lip landmark-guided visual clues instead of mouth-cropped images as input features.
Outcome:	Experimental results show that the proposed approach reduces speaker-specific appearance characteristics in cross-speaker scenarios.

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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li

STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions.
Approach:	They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations.
Outcome:	The proposed framework measures the agent's higher-order social cognition in multi-turn conversations.

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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, Yiqun Liu

DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)

Challenge:	Existing statute retrieval benchmarks emphasize formal and professional queries from sources like bar exams and legal case documents . existing retrieval approaches that lack domain-specific knowledge may struggle to capture the meanings of specialized terms accurately.
Approach:	They propose a dataset that captures the complexity and diversity of real queries from the general public.
Outcome:	The proposed dataset captures the complexity and diversity of real queries from the general public.

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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng

Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (2023.emnlp-main)

Challenge:	Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages.
Approach:	They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation .
Outcome:	The proposed model outperforms existing state-of-the-art methods on two benchmark datasets.

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Junhan Yang, Zheng Liu, Chaozhuo Li, Guangzhong Sun, Xing Xie

PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)

Challenge:	Existing PLMs are infeasible for processing long documents due to computational costs and incomprehensive document understanding.
Approach:	They propose a retrieval model that models local semantics and global context semantics in a tightly-coupled manner.
Outcome:	The proposed model overcomes three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.

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Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Robustness in NLP

Challenge:	Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples.
Approach:	They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen.
Outcome:	The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters.

Matching-oriented Embedding Quantization For Ad-hoc Retrieval (2021.emnlp-main)

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Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, Xing Xie

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

Challenge:	Product quantization (PQ) is a widely used technique for ad-hoc retrieval.
Approach:	They propose a match-oriented product quantization with a multinoulli contrastive loss objective.
Outcome:	The proposed method maximizes matching probability of query and ground-truth key, compared with previous methods on non-supervised datasets.

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

Text Generation

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

Fine-grained Interest Matching for Neural News Recommendation (2020.acl-main)

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Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie

Representation Learning

Challenge:	Existing studies represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.
Approach:	They propose a Fine-grained interest matching method for neural news recommendation based on multi-level representations and fine-grain matching between segment pairs of each browsed news and the candidate news at each semantic level.
Outcome:	The proposed model can capture more fine-grained interest matching signals by performing interactions between each pair of news at multi-level semantic granularities.

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)

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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng

Challenge:	Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data.
Approach:	They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data.
Outcome:	The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard.

Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization (2024.findings-emnlp)

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Shitong Duan, Xiaoyuan Yi, Peng Zhang, Yan Liu, Zheng Liu, Tun Lu, Xing Xie, Ning Gu

Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)

Challenge:	Existing methods to steer LLMs towards human preference suffer from noisy positive-negative training pairs.
Approach:	They propose a distributional preference optimization method which maximizes discrepancy between dispreferred responses and generated non-negative ones.
Outcome:	The proposed method achieves comparable generation quality and surpasses the latest strong baselines in producing less harmful and more informative responses with better training stability and faster convergence.

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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han

Structured Data in NLP

Challenge:	Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems.
Approach:	They propose to augment large language models with text units retrieved from external knowledge corpora to alleviate the issue.
Outcome:	The proposed framework outperforms baselines on GRBench with three LLMs and shows that iterative reasoning outperformed the baselines.

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

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Qiming Xie, Wenjie Zheng, Xiangqing Shen, Rui Xia