AI Research
TIGER: Traceable Inference with Graph-Based Evidence Routing for Mitigating Hallucinations in Multimodal Generation
We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inference-time repair methods often generate feedback by jointly conditioning on the input and the current output. This design has ...
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ArXiv CS.AI
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ArXiv CS.AI
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TIGER introduces a novel graph-based approach to identify and repair unsupported facts in AI-generated content, significantly improving the reliability of multimodal AI systems. This method's ability to localize and rank claims based on evidence is crucial for building trustworthy AI that grounds its outputs in provided data.
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