4 papers
Eigenvalue Calibration for Semantic Embeddings of Large Language Models
Sebastian G. Gruber, Nassim Walha, Francis Bach +1
Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in stat…
From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
Azza Jenane, Nassim Walha, Lukas Kuhn +1
Large Language Models (LLMs) that can express interpretable and calibrated uncertainty are crucial in high-stakes domains. While methods to compute uncertainty post-hoc exist, they…
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
Disentangling Mean Embeddings for Better Diagnostics of Image Generators
Sebastian G. Gruber, Pascal Tobias Ziegler, Florian Buettner
The evaluation of image generators remains a challenge due to the limitations of traditional metrics in providing nuanced insights into specific image regions. This is a critical p…