arXiv:2606.09578v1 Announce Type: new Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly evaluated on table reasoning tasks, but the role of table representation remains under-explored. In practice, the same table content may appear in different structural formats, such as HTML, Markdown, and LaTeX, or as rendered images. However, existing evaluations often let content, format, layout, and modality vary together, making it difficult to isolate representation effects. We introduce TABVERSE, a controlled multimodal table benchmark that aligns the same table
The proliferation of LLMs and VLMs necessitates more robust and nuanced evaluation methods as their integration into complex tasks increases.
Improved benchmarking for table understanding across formats directly impacts the reliability and capability of AI systems in enterprise and data-driven applications, a critical frontier for AI development.
The explicit focus on isolating table representation effects provides a clearer path for developing more versatile and format-agnostic LLMs and VLMs.
- · AI developers
- · Data analysis software
- · Businesses relying on data extraction
- · Legacy data processing methods
More accurate and versatile AI models for structured data processing will emerge.
This will accelerate automation in knowledge work that heavily relies on diverse data formats.
Enhanced table understanding could enable AI systems to infer relationships and insights from previously disparate datasets, unlocking new forms of intelligence.
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Read at arXiv cs.AI