Papers by Yiwu Zhong
DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models (2026.findings-acl)
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| Challenge: | Existing data filtering methods are expensive because they are trained on the same data they are meant to screen. |
| Approach: | They propose to use off-the-shelf pretrained models that have never seen the target data to select training samples for larger and stronger multimodal models without task-specific training. |
| Outcome: | The proposed method can achieve comparable or even better results than those trained on the full dataset in standard VQA and math benchmarks. |
Beyond Embeddings: The Promise of Visual Table in Visual Reasoning (2024.emnlp-main)
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| Challenge: | Visual representation learning has been a cornerstone in computer vision for decades. |
| Approach: | They propose a visual representation tailored for visual reasoning that provides instance-level world knowledge and detailed attributes that are essential for visual reason. |
| Outcome: | The proposed visual tables outperform existing models on 11 visual reasoning benchmarks. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision (2025.emnlp-industry)
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Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Recent advances in outcome-supervised reinforcement learning (RL) have shown strong performance, but this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. |
| Approach: | They propose a model that models RAG as a Markov Decision Process (MDP) and introduces an efficient pruning strategy to optimize data expansion. |
| Outcome: | The proposed model outperforms existing methods and achieves an average performance improvement of 6.2% across six datasets. |
Enhancing Temporal Modeling of Video LLMs via Time Gating (2024.findings-emnlp)
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| Challenge: | Existing Video Large Language Models neglect temporal information in video data, leading to struggles with temporal-aware video understanding. |
| Approach: | They propose a Time Gating Video LLM (TG-Vid) that employs a time gating module to enhance temporal modeling. |
| Outcome: | The proposed model outperforms existing Large Language Models on video-and-language tasks and ablation studies show that the model outpersforms the existing models. |