ChartAnchor: Chart Grounding with Structural-Semantic Fidelity
This addresses the need for rigorous chart grounding benchmarks in scientific, financial, and industrial domains, though it is incremental as it builds on existing evaluation frameworks.
The authors tackled the problem of evaluating multimodal large language models' structured chart comprehension by proposing ChartAnchor, a comprehensive benchmark with 8k+ chart-table-code triples spanning 30 chart types, which revealed critical limitations in numerical precision and code synthesis.
Recent advances in multimodal large language models (MLLMs) highlight the need for benchmarks that rigorously evaluate structured chart comprehension.Chart grounding refers to the bidirectional alignment between a chart's visual appearance and the structured semantics. This task requires models to produce a symbolic specification that faithfully captures the chart's visual and structural intent, while also recovering the underlying tabular data with precise values and relationships. Chart grounding directly reflects a model's capabilities in numerical reasoning, multimodal alignment, and structural reconstruction, and has several important applications in real-world scenarios.Existing benchmarks, constrained by narrow chart diversity, isolated tasks, and incomplete evaluation frameworks, fail to holistically assess grounding. To address this, we propose ChartAnchor, a comprehensive benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources. ChartAnchor introduces two complementary tasks: chart-to-code generation (synthesizing executable code to replicate charts) and controlled chart-to-table reconstruction (extracting exact data with predefined headers), enabling cross-validation of visual and numerical fidelity. A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness. Extensive experiments on MLLMs reveal critical limitations in numerical precision and code synthesis, emphasizing the need for structured reasoning beyond surface-level perception. By unifying symbolic and data-driven grounding, ChartAnchor establishes a rigorous foundation for chart grounding, offering meaningful insights for advancing MLLMs in scientific, financial, and industrial domains.