Papers by Rui Ming
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
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| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)
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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng
| Challenge: | Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries. |
| Approach: | They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. |
| Outcome: | The proposed framework outperforms strong baselines while being robust against various NOTA rates. |
Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts (2025.acl-long)
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| Challenge: | Large language models have shown significant promise in question-answering tasks . noisy reference documents hinder performance of LLMs, causing disproportionate attention to irrelevant content . |
| Approach: | They propose an adaptive large language model that allocates disproportionate attention to irrelevant documents . they use transformers to train the model and integrate it into pre-trained Transformer blocks . |
| Outcome: | The proposed model outperforms state-of-the-art models on noisy-context benchmarks. |
Multi-Programming Language Sandbox for LLMs (2025.acl-demo)
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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang
| Challenge: | MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). |
| Approach: | They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models. |
| Outcome: | The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs). |
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)
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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
| Challenge: | Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. |
| Approach: | They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs. |
| Outcome: | The proposed method significantly outperforms existing temporal knowledge graph embedding models. |
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)
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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng
| Challenge: | Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency. |
| Approach: | They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment . |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples. |
TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)
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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng
| Challenge: | Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users. |
| Approach: | They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification. |
| Outcome: | The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots. |
MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown impressive progress in mathematical problem-solving . current approaches to enhance mathematical reasoning focus on instance-level modifications . |
| Approach: | They propose a framework that enhances mathematical reasoning through cross-problem instruction synthesis. |
| Outcome: | The proposed framework boosts mathematical reasoning by 18.0 points while maintaining high data efficiency. |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
PICD-Instruct: A Generative Instruction Learning Framework for Few-Shot Multi-Intent Spoken Language Understanding (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have utilized instruction learning frameworks to model intent-slot interdependencies, typically requiring abundant data for effective training. |
| Approach: | They propose a generative framework based on Basic Instructions (BI), Pairwise Interaction Instructions and Contrastive Distinct Instructions to solve these challenges. |
| Outcome: | The proposed framework achieves state-of-the-art performance on public datasets. |
MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting. |
| Approach: | They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions. |
| Outcome: | The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks. |
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks (2020.acl-main)
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| Challenge: | Existing methods for training generative models with minimal corpus are difficult . fine-tuning distinguishes tasks from parameter perspective but ignores model-structure perspective . |
| Approach: | They propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. |
| Outcome: | The proposed method outperforms baselines on two datasets in task consistency, response quality, diversity and consistency. |
ECoK: Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024.findings-acl)
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Zhunheng Wang, Xiaoyi Liu, Mengting Hu, Rui Ying, Ming Jiang, Jianfeng Wu, Yalan Xie, Hang Gao, Renhong Cheng
| Challenge: | Existing knowledge graphs focus on the representation and reasoning of general factual knowledge, while there are significant deficiencies in the understanding and reasoning for emotional knowledge. |
| Approach: | They propose a commonsense knowledge graph that can be used to represent emotional knowledge by combining theories from psychology, cognitive science, and linguistics. |
| Outcome: | The proposed model surpasses GPT-4-Turbo in the emotion-related tasks. |