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AI Research
arXiv CS.AI·May 25·4 min read
DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Implement DocVAL for better spatial reasoning in compact VLMs.
- Utilize teacher-generated CoT supervision for training.
- Adopt the new mAP metric for evaluating localization.
- Leverage the dual-mode validator to enhance training signal quality.
How It Works
DocVAL transfers spatial reasoning from larger teacher models to smaller student models through a validated chain-of-thought (CoT) approach. It combines teacher-generated supervision with a dual-mode validator that filters out low-quality training signals, providing pixel-level feedback.
This two-stage training process allows for iterative refinement, ensuring that the final model can operate efficiently without relying on text detection during inference.