Generative Score Inference for Multimodal Data
Summary: arXiv:2603.26349v1 Announce Type: cross
Abstract
Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capable of constructing statistically valid and informative prediction and confidence sets across a wide range of multimodal learning problems.
Introduction to Generative Score Inference
Generative Score Inference (GSI) represents a significant advancement in the field of uncertainty quantification within supervised learning. Traditional methods often come with constraints that hinder their application to various data types and tasks. GSI addresses these issues by:
- Utilizing synthetic samples generated by deep generative models.
- Approximating conditional score distributions for improved accuracy.
- Facilitating precise uncertainty quantification without imposing restrictive assumptions.
Methodology
The core of GSI’s methodology lies in its ability to leverage generative models to create synthetic data that helps estimate the underlying score distributions. This approach allows for flexibility and adaptability in various multimodal contexts. GSI consists of several steps:
- Data Generation: Synthetic samples are produced using advanced generative models, which serve as a foundation for the inference process.
- Score Estimation: The method approximates the conditional score distributions from these synthetic samples, enhancing the model’s ability to quantify uncertainty accurately.
- Prediction and Confidence Sets: GSI constructs statistically valid prediction and confidence sets that provide insights into the reliability of the model’s outputs.
Empirical Validation
To validate the effectiveness of GSI, we conducted experiments in two representative scenarios:
- Hallucination Detection in Large Language Models: GSI demonstrated state-of-the-art performance in identifying inaccuracies or “hallucinations” produced by large language models.
- Uncertainty Estimation in Image Captioning: The framework provided robust predictive uncertainty, significantly improving the reliability of image captioning tasks.
Conclusion
The findings from our experiments underscore the potential of Generative Score Inference as a versatile and powerful framework for uncertainty quantification in multimodal learning contexts. The performance of GSI is notably influenced by the quality of the underlying generative model, suggesting that advancements in generative modeling can further enhance its efficacy. By addressing the limitations of traditional approaches, GSI stands to significantly improve trustworthiness and decision-making in various applications involving complex data types.