DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering

arXiv:2609.20574 · cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.CV

Submitted: 2026-09-17

Updated: 2026-09-22

Code: https://github.com/huggingface/peft

Project page: https://aimagelab.github.io/DocAttriBench

Terminology

• Grounding — 133× in this paper · explained in 3 episodes
• Benchmarking — 49× in this paper · explained in 4 episodes
• Benchmark — 33× in this paper · explained in 2 episodes
• Accuracy — 26× in this paper · explained in 1 episode
• Perplexity — 18× in this paper · explained in 3 episodes
• Fine-Tuning — 17× in this paper · explained in 5 episodes
• Abstraction — 13× in this paper · explained in 1 episode
• Visual Answer Grounding — 10× in this paper
• Visual Question Answering — 9× in this paper
• F1 Score — 6× in this paper · explained in 3 episodes
• Hallucination — 6× in this paper · explained in 4 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Multimodal Large Language Model — 5× in this paper
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Document Understanding Dataset and Evaluation — 1× in this paper
• Elias — 1× in this paper · explained in 2 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Docling Technical Report
• Qwen2.5-VL Technical Report
• PaLI-X: On Scaling up a Multilingual Vision and Language Model
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• LLaVA-OneVision: Easy Visual Task Transfer
• VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
• CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
• A Survey of Deep Learning Approaches for OCR and Document Understanding
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• FineVision: Open Data Is All You Need
• Qwen3 Technical Report
• mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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