Building 7K+ High-Complexity SlideVQA Tasks Across 20+ Knowledge Domains
Created expert-verified multimodal QA prompts from real-world slide decks, targeting reasoning failures in large multimodal models (LMMs) across business, STEM, finance, and general knowledge.
7000+
SOTA model-breaking QA samples: Created across 20+ knowledge domains with slide-grounded visual reasoning.
3-tier
Difficulty structure: Prompts were labeled Easy, Medium, or Hard based on reasoning complexity, number of steps, and the number of slides and visuals referenced.
100%
Visual grounding required: All prompts require visual references, such as charts, infographics, maps, or diagrams.
The Challenge
Frontier LMMs often underperform on real-world business slides and STEM visualizations due to limitations in grounding, counting, layout parsing, and multi-hop reasoning.
The client needed:
- A scalable dataset of SOTA model-breaking prompt + ideal response pairs grounded in real slide visuals
- Each prompt to require chain-of-thought reasoning across one or more charts, images, tables, or diagrams
- Tasks categorized into Easy, Medium, and Hard levels based on reasoning depth and source complexity
- Fully auditable ideal responses built to expose model hallucination, miscounting, and visual-text fusion failure
- Prompts that required cross-referencing across multiple slides or visuals, not just single-image QA
The Approach
Dataset scope & structure
- 20+ domains: Business, Engineering, Finance, Economics, Science, History, Health, Tech, and more
- 3 prompts per slide deck: One each of Easy, Medium, and Hard, based on a combination of reasoning complexity, number of logical steps, and the number of slides and visuals that must be referenced or cross-linked
- Visual types: Stacked bar charts, floor plans, comparison tables, infographics, and annotated graphs
- Reasoning types:
- Multi-hop inference across slides
- Numerical operations over multiple data points
- Visual alignment + semantic matching
- Visual approximations and estimation when exact values are unavailable
- Cross-slide comparison of categories, growth trends, or attribute distributions
Annotation and QA pipeline
Each prompt + ideal response pair was created with:
- Realistic task phrasing: User-oriented questions like CAGR comparison, segmentation delta, floor plan dimension reasoning
- Minimum 4-step chain-of-thought per task; up to 25 for complex logic
- Grounding requirement: Each task must cite at least one visual and state how it’s used
- QA rubric alignment: Pass/fail based on reasoning trace, factuality, calculation clarity, and visual reference validity
Key Results
- Created 7K+ visual-question-answer tasks with CoT explanation traces
- Built a multimodal benchmark layer on top of SlideVQA (AAAI 2023 benchmark) with human-authored ideal responses
- Successfully exposed known model weaknesses, including:
- Stacked chart misreadings (e.g., incorrect value segmenting)
- Floor plan misunderstanding (e.g., excluding embedded structures like closets or AC units)
- Visual hallucinations when faced with ambiguous color-coded legends
- Model inability to link multiple slide visuals for multi-step calculations
The Outcome
The client can now:
- Train or evaluate LMMs on tasks that require dense document + visual fusion
- Stress-test models on slide decks that mirror real enterprise content
- Fine-tune chain-of-thought response quality using ideal answer traces grounded in visuals
- Extend the SlideVQA benchmark with expert-authored tasks that meet high school to postgraduate reasoning complexity
Test your model’s slide reasoning with grounded QA
Get sample prompts that expose grounding gaps, visual misreads, and CoT breakdowns.
Request SampleFAQ
What’s in the SlideVQA sample?
Each sample includes a user-facing prompt, the source slide image, visual cue references, a multi-step chain-of-thought answer, and rubric-validated QA notes.
What types of visuals are supported?
Stacked charts, line graphs, blueprints, tables, maps, infographics, multi-part slide decks.
Do you support OCR or layout parsing?
Yes. Prompts are designed to exploit known weaknesses in layout, alignment, counting, segment grouping, and cross-referencing across slides.
Can we train or fine-tune on these tasks?
Yes. The dataset supports both eval and SFT/RLHF fine-tuning use cases with ideal responses.
What’s the NDA process?
A standard mutual NDA; Turing returns countersignature within one business day.
How fast can I get a sample?
Within 3 business days of NDA execution.
Ready to stress-test your vision-language model?
Get grounded SlideVQA prompts built to expose reasoning, alignment, and layout comprehension failures.