1 citations · 1 across the 3 of their papers we have counts for
12 papers
Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework
Yuchen He, Peizhi Ying, Liqi Cheng +4
Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign. Existing interactive tools are re…
Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation
Dazhen Deng, Zhaoping He, Xin Qian +3
Large language models (LLMs) can generate individual charts, but coordinated multi-view visualizations (CMVs), where views share data flows and cross-view interactions, remain out…
ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL
Zhaorui Yang, Huawei Zheng, Sen Yang +14
Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large…
GraphTide: Augmenting Knowledge-Intensive Text with Progressive Nested Graph
Xin Qian, Dazhen Deng, Zhaoping He +3
Knowledge-intensive text usually contains fruitful entities and complex relationships, such as academic articles and scientific exposition. Reading and comprehending such texts oft…
CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
Dazhen Deng, Sen Yang, Yuchen He +2
Current chart-related tasks, such as chart generation (NL2Chart), chart schema parsing, chart data parsing, and chart question answering (ChartQA), are typically studied in isolati…
KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models
Zhenning Chen, Hanbei Zhan, Yanwei Huang +4
Large Language Models (LLMs) demonstrate exceptional capabilities in factual question answering, yet they sometimes provide incorrect responses. To address this issue, knowledge ed…