Evaluating the Capability of Video Question Generation for Expert Knowledge Elicitation
Revolutionizing Expert Knowledge Elicitation with Advanced VQG Evaluation
This analysis focuses on a novel approach to evaluating Video Question Generation (VQG) models, specifically their ability to elicit 'unseen knowledge' from human experts. By proposing a retrieval-based evaluation protocol and introducing the EgoExoAsk dataset, the research provides a quantitative framework to assess question quality. Key findings demonstrate that models with richer contextual access (e.g., video captions) perform better, supporting the protocol's validity and offering a path for continuous VQG model improvement in expert knowledge elicitation.
Executive Impact
This research introduces a robust framework for assessing the effectiveness of AI in generating high-quality questions for expert knowledge elicitation, driving significant improvements in data collection and operational efficiency.
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Improved Question Quality
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Hours of Expert Engagement Saved
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Efficiency Gain in Knowledge Elicitation
Deep Analysis & Enterprise Applications
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0.6433
Trained Retriever R@10 for QA (vs. 0.3286 Zero-shot)
Impact of Richer Context
Providing video captions improves R@1 and MeanR for all VLLMs by about 0.03 and 1.5 on average compared to naive models. This demonstrates the critical role of contextual information in generating higher-quality questions that effectively elicit expert knowledge.
Highlight: Video captions led to a noticeable performance boost across VQG models.
Retrieval-based Evaluation Protocol
VQG Model Generates Question
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QA Retriever Matches to Answers
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Retrieval Rank Assigned
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Question Quality Assessed (Recall@k, MeanR)
EgoExoAsk Dataset Construction Flow
Expert Comments Formatting
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Question Generation
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Question Verification & Regeneration
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Video Segment Assignment
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EgoExoAsk Dataset
| Method | R@1 Score |
|---|---|
| Gold Standard | 0.6631 |
| Trained Retriever (Zero-shot) | 0.1347 |
| Naive VLLM (Exo) | 0.0614 |
| VLLM w/ Caption (Exo) | 0.0946 |
| VLLM w/ RAG (Exo) | 0.0784 |
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Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Implementation Roadmap
Our phased approach ensures a smooth and effective integration of advanced VQG evaluation into your existing workflows, maximizing impact with minimal disruption.
Phase 1: Dataset Integration
Integrate EgoExoAsk dataset for training and benchmarking, ensuring data quality and alignment with expert commentary.
Phase 2: Retriever Fine-tuning
Optimize QA retriever performance on the EgoExoAsk dataset to accurately emulate expert recall and enhance retrieval scores.
Phase 3: VQG Model Iteration
Experiment with different VQG models and contextual inputs, using the new metric to guide improvements in question generation quality.
Phase 4: Expert Validation & Deployment
Conduct human expert validation to cross-check quantitative results and prepare for real-world deployment of enhanced VQG systems.
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