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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study
TL;DR
A new self-verification pipeline for video question answering systems effectively identifies unsupported claims, achieving a 79% catch rate for fabricated citations. This approach enhances user trust by ensuring accuracy in timestamped responses.
✦ Why It Matters
Implement this self-verification pipeline to enhance the accuracy of your video question answering systems today.
Key Takeaways
- Adopt a self-verification approach for video question answering systems.
- Utilize a natural language inference model for claim verification.
- Evaluate timestamp citations to ensure accuracy in responses.
- Access and implement the released code for practical applications.
How It Works
The self-verification pipeline operates by first generating answers with timestamp citations using a retrieval-augmented language model. Each timestamped claim is then independently verified against the cited video frame.
Instead of relying on the vision model's confidence, which can be misleading, the pipeline employs a natural language inference model to assess the validity of claims, ensuring that only accurate information is presented to users.