Generative Score Inference for Multimodal Data

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• Generative Score Inference (GSI) is a flexible inference framework for constructing statistically valid and informative prediction and confidence sets across multimodal learning problems.method 95%
• GSI utilizes synthetic samples generated by deep generative models to approximate conditional score distributions for precise uncertainty quantification.method 95%
• GSI achieves state-of-the-art performance in hallucination detection in large language models.result 90%
• GSI demonstrates robust predictive uncertainty in image captioning.result 90%
• The performance of GSI is positively influenced by the quality of the underlying generative model.technical 90%
• GSI accommodates diverse data modalities including tabular, images, and text, and various generative model families like diffusion, normalizing flows, and autoregressive models.technical 95%
• GSI delivers shorter prediction intervals than CUQR while matching or exceeding its coverage.result 85%
• Conditional diffusion models are a robust default generator for GSI, showing stable subgroup coverage and competitive interval lengths compared to other generative models.technical 85%

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