Ruiyang-061X/Uncertainty-o
✨ Official code for our paper: "Uncertainty-o: One Model-agnostic Framework for Unveiling Epistemic Uncertainty in Large Multimodal Models".
This project helps researchers and developers working with Large Multimodal Models (LMMs) understand how confident these models are in their responses. It takes a multimodal prompt (like an image and text query) and an LMM, then outputs a quantifiable measure of the model's uncertainty about its answer. This is useful for anyone evaluating LMM performance, especially in scenarios where accuracy and reliability are critical.
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Use this if you need to detect and understand 'hallucinations' or unreliable outputs from Large Multimodal Models when processing mixed image and text inputs.
Not ideal if you are working with purely text-based models or if you need a solution that directly improves LMM accuracy rather than just measuring its uncertainty.
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Python
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Mar 13, 2025
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