calibration

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

This framework works as a form of user/machine calibration, with a focus on user-context and user-intent, deconstructing your ideas logically from A to B to Z.

Multi-modal AI-generated content detection: image, video, and audio. Benchmarks, training code (DINOv2, DINOv3, ReStraV, BreathNet), and evaluation pipeline for real vs. synthetic classification with calibration-aware metrics.

Calibrating LLMs with Information-Theoretic Evidential Deep Learning (ICLR 2025)