This work introduces ChartAnno, a comprehensive benchmark for evaluating MLLMs on chart annotation generation, and develops a multidimensional evaluation framework combining rule-based and LLM-judged metrics to assess execution, structural compliance, semantic consistency, and design effectiveness.
Zhenghan Chen, Zekai Shao, Lidan Tan et al.· 0 citations
The rapid evolution of artificial intelligence (AI) tools has demonstrated immense potential to enhance societal well-being and operational efficiency. However, the inherent unreliability and uncertain operational consequences of modern AI systems, typified by large language models (LLMs), have created a significant ba...
Yi-Xuan Yuan, Dedai Wei, Chudong Qian et al.· 0 citations
Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart sem...
Zhenghan Chen, Zekai Shao, Lidan Tan et al.· 0 citations
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