Papers by Xiao Zong
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization (2023.acl-long)
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| Challenge: | Existing multimodal summarization models ignore the contribution of visual modalities . we propose a novel contribution network to consider different contributions of images . |
| Approach: | They propose a Coarse-to-Fine contribution network for multimodal summarization to consider different contributions of images for summarizing. |
| Outcome: | The proposed system outperforms baselines on the visual and textual modalities. |
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)
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| Challenge: | Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability. |
| Approach: | They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs. |
| Outcome: | The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese . |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Pay More Attention to Images: Numerous Images-Oriented Multimodal Summarization (2025.naacl-long)
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| Challenge: | Existing multimodal summarization approaches struggle with scenarios involving multiple images as input. |
| Approach: | They propose a task to generate multimodal summaries by integrating multiple images as input . they propose 'multimodal information evaluation' method that measures differences between generated summary and input based on multimodal input - and compares various methods . |
| Outcome: | The proposed method correlates more closely with human judgments than five widely used metrics . |