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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
A Table Is Worth 64 Tokens: Pixel-level Compression for Multi-Table Document Question Answering
TL;DR
A novel two-step method for question answering over documents with multiple tables uses pixel-level compression to enhance efficiency. This approach saves 41% in token usage while improving accuracy by 7 points compared to traditional methods.
✦ Why It Matters
Implement this two-step method to optimize token usage and improve accuracy in document question answering tasks.
Key Takeaways
- Use pixel-level compression for efficient document processing.
- Implement a two-step method for improved question answering.
- Evaluate VLMs with various visual-token budgets.
- Focus on native resolution for critical reasoning tasks.
How It Works
The two-step method first identifies relevant tables from a pixel-compressed context, allowing the model to focus on only the necessary information. After identifying these tables, the model then processes them at native resolution, which enhances readability and reasoning accuracy.