Papers by Xudong Liu
Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding (2026.eacl-srw)
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| Challenge: | Recent studies improve visual contrastive decoding (VCD) by constructing more informative auxiliary views. |
| Approach: | They propose to construct an object-aligned auxiliary view that disrupts unsupported tokens and produces a stronger contrast signal. |
| Outcome: | Empirically, the proposed method shows consistent gains on two popular object hallucination benchmarks across two MLLMs. |
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)
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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai
| Challenge: | Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge. |
| Approach: | They propose a recurrent inductive bias that aligns with the recursive nature of programming logic. |
| Outcome: | The proposed model achieves comparable performance to standard dense models with more parameters. |
Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)
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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang
| Challenge: | Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process. |
| Approach: | They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors. |
| Outcome: | The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs. |
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)
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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu
| Challenge: | Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization. |
| Approach: | They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training. |
| Outcome: | The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization. |
Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)
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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu
| Challenge: | Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
| Approach: | They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness. |
| Outcome: | Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| 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. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)
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Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, Jianing Qiu
| Challenge: | Existing benchmarks for multimodal large language models do not capture real-world clinical complexity. |
| Approach: | They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case. |
| Outcome: | The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes. |
| Approach: | They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques. |
| Outcome: | The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks. |
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing the performance of large language models require expensive manual annotations. |
| Approach: | They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence. |
| Outcome: | The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method. |
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking (2022.findings-acl)
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| Challenge: | Recent work on entity linking has focused on the zero-shot scenario where at test time the entity mention to be labelled is never seen during training. |
| Approach: | They propose a transformational biencoder that integrates a transform into BERT to perform a zero-shot transfer from the source domain to the target domain. |
| Outcome: | The proposed model performs a zero-shot transfer from the source domain to the target domain on a benchmark dataset and achieves new state-of-the-art. |
ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning (2025.emnlp-demos)
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Yichen Lu, Wei Dai, Jiaen Liu, Ching Wing Kwok, Zongheng Wu, Xudong Xiao, Ao Sun, Sheng Fu, Jianyuan Zhan, Yian Wang, Takatomo Saito, Sicheng Lai
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in Machine Translation (MT) tasks. |
| Approach: | They propose a translation agent system designed for multimodal input that leverages visual and contextual background information to enhance the translation process. |
| Outcome: | The proposed translation agent achieves significantly higher translation quality in subtitle generation and general translation tasks compared to previous state-of-the-art systems. |
LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification (2025.findings-acl)
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| Challenge: | Existing schema linking methods are not able to handle complex SQL queries. |
| Approach: | They propose a new algorithm that transforms SQL queries into grammatically verifiable sub-queries which are arranged sequentially to reflect single-hop reasoning steps. |
| Outcome: | The proposed algorithm achieves significant performance gains on the BIRD dataset and surpasses schema linking methods at comparable or better cost. |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
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Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)
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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang
| Challenge: | Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question. |
| Approach: | They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance. |
| Outcome: | Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction (2025.findings-naacl)
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| Challenge: | PuzzleGPT is a modular and iterative puzzlesolving method for predicting time and location from images. |
| Approach: | They propose to formalize this ability into core skills and implement it using different modules in an expert pipeline called PuzzleGPT. |
| Outcome: | The proposed method outperforms large VLMs and finetuned models on TARA and WikiTilo and rivals or surpasses finetuned models. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |
CodeV: Issue Resolving with Visual Data (2025.findings-acl)
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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang
| Challenge: | Large Language Models (LLMs) have expanded to more complex repository-level tasks. |
| Approach: | They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues. |
| Outcome: | The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data. |