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
This paper introduces a socio-economic framework that complements existing technical safeguards by transferring and absorbing the residual financial consequences of AI adoption through insurance, and develops an LLM-driven agent-based social simulation (LABSS) system.
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.