Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
TL;DR
Existing evaluations of Large Language Models (LLMs) and Vision-Language Models (VLMs) on table reasoning tasks do not isolate the effects of table representation across different formats. TABVERSE is introduced as a controlled benchmarking framework that allows for the assessment of table understanding in various structural formats like HTML and Markdown.
✦ Why It Matters
Engineers can use TABVERSE to better evaluate and improve model performance on table reasoning tasks across different formats.
Key Takeaways
- Evaluate models using TABVERSE for better insights on table understanding.
- Prefer structured text formats over images for improved model performance.
- HTML is often the most effective format for table representation.
- Be cautious with row-sensitive tasks and LaTeX reconstruction challenges.
How It Works
TABVERSE systematically aligns the same table content across multiple formats, allowing for controlled evaluations of how different representations affect model performance. By tagging questions with categories and difficulty levels, it enables a nuanced analysis of model capabilities in understanding and reconstructing table data.