Papers by Ning Xu
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation (2026.acl-long)
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| Challenge: | Prior work has shown that safety behaviors are governed by low-rank structures . Low-Rank Adaptation (LoRA) consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks . |
| Approach: | They propose a safety alignment system that disentangles safety-relevant directions into monosemantic features and constructs an interpretable safety subspace from SAE directions. |
| Outcome: | Empirically, the proposed model achieves 99.6% safety rates across multiple model families and scales . low-rank Adaptation consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks compared with previous methods . |
Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning (2026.findings-acl)
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| Challenge: | Existing Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) systems insufficiently model the interaction between query semantics and relation types, resulting in imprecise subgraph retrieval and unstable reasoning. |
| Approach: | They propose a retrieval framework that integrates query semantics and relation embeddings directly into the attention mechanism. |
| Outcome: | Experiments on WebQSP and CWQ establish new state-of-the-art results in both Triple Recall and Answer Recall. |
Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)
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| 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. |
CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations (2025.findings-acl)
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| Challenge: | Existing studies have isolated LLM jailbreak attacks and defenses . a new framework combines attack and defense to protect against malicious queries . |
| Approach: | They propose a framework that combines attack and defense to protect the Large Language Model (LLM) by embedding harmful problems into the safe area. |
| Outcome: | The proposed framework achieves an average jailbreak success rate of 88.85% across three popular LLMs while the defense success rate reaches an average of 84.17%. |
Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)
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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu
| Challenge: | Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario. |
| Approach: | They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus. |
| Outcome: | The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge. |
Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations (2021.findings-emnlp)
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| Challenge: | Existing methods to classify events using syntactic dependency relations have not been developed. |
| Approach: | They propose a model which combines syntactic dependency relations with attention-based dynamic tensors to mine node-to-node latent dependency relations via self-attention mechanism. |
| Outcome: | The proposed model improves on the ACE2005 dataset and compares with baseline models. |
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text (2022.findings-acl)
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| Challenge: | Existing models for toxic span detection only classify text snippets as offensive or not . a novel model seeks to simultaneously predict offensive words and opinion phrases . |
| Approach: | They propose a novel model that seeks to predict offensive words and opinion phrases simultaneously . they also introduce a regularization mechanism to encourage consistency of the model predictions . |
| Outcome: | The proposed model performs well compared to baselines on toxic span detection tasks . it predicts offensive words and opinion phrases to leverage inter-dependencies . |
Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)
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| Challenge: | Existing methods for hierarchical text classification are limited and lack holistic structural information. |
| Approach: | They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features. |
| Outcome: | The proposed model improves on three benchmark datasets. |
iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)
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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang
| Challenge: | Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. |
| Approach: | They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure. |
| Outcome: | The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users. |
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks. |
| Approach: | They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités. |
| Outcome: | The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them. |
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)
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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan
| Challenge: | Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost . |
| Approach: | They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation . |
| Outcome: | GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%. |
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)
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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou
| Challenge: | Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. |
| Approach: | They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks. |
| Outcome: | The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
| Approach: | They propose a self-supervised taxonomy expansion method that ranks taxonomies by ranking them . they use pre-trained contextual encoders to train the model with dynamic margin loss . |
| Outcome: | The proposed method outperforms state-of-the-art taxonomy expansion methods by 14.3% and 15.8% on public benchmarks. |
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)
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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu
| Challenge: | Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data . |
| Approach: | They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference. |
| Outcome: | The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets. |
Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)
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Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim
| Challenge: | Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning. |
| Approach: | They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling. |
| Outcome: | The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. |
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)
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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English. |
| Approach: | They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries. |
| Outcome: | The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation. |
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
| Approach: | They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data. |
| Outcome: | The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc. |
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)
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| Challenge: | Recent work shows that Code Large Language Models can address a wide range of code-related tasks. |
| Approach: | They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models. |
| Outcome: | The proposed model outperforms open-source models in generalization ability across code-related tasks. |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
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| Challenge: | Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters. |
| Approach: | They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace. |
| Outcome: | The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance. |
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation (2020.acl-main)
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| Challenge: | Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS) however, they suffer from the cross-domain issue when they come to processing of out-of-domain data. |
| Approach: | They propose to use Chinese word as a target domain for distant annotation and adversarial training to reduce noise and maximize utilization of the source domain information. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on real-world datasets and significantly outperformed previous state- of-the art methods. |
iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use (2025.emnlp-main)
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Yirong Zeng, Xiao Ding, Yuxian Wang, Weiwen Liu, Yutai Hou, Wu Ning, Xu Huang, Duyu Tang, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Synthesizing tool-use data through real-world simulations is effective for enhancing large language models (LLMs) however, training gains decay as synthetic data increases, and the model struggles to benefit from more synthetic data. |
| Approach: | They propose an iterative reinforced fine-tuning strategy to improve LLMs with external tools to augment their capabilities. |
| Outcome: | The proposed method achieves 13.11% better performance than the same-size base model and outperforms larger open-source and closed-source models. |