A model-agnostic framework that uses synthetic sampling to provide statistically valid uncertainty quantification and hallucination detection for multimodal models.
March 30, 2026
Original Paper
Generative Score Inference for Multimodal Data
arXiv · 2603.26349
The Takeaway
Reliable uncertainty estimation is a critical hurdle for deploying LLMs and image captioning models in high-stakes environments. By using the model’s own generative output to construct confidence sets, GSI provides a versatile way to flag hallucinations and quantify reliability without requiring specialized labels or architectural changes.
From the 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