1 citations · 1 across the 2 of their papers we have counts for
8 papers
Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger +1
Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of hallucinations, known as confa…
xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Maximilian Beck, Kajetan Schweighofer, Sebastian Böck +2
Scaling laws play a central role in the success of Large Language Models (LLMs), enabling the prediction of model performance relative to compute budgets prior to training. While T…
Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi +3
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantific…
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
Kajetan Schweighofer, Barbara Brune, Lukas Gruber +11
There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially accep…
ImageSet2Text: Describing Sets of Images through Text
Piera Riccio, Francesco Galati, Kajetan Schweighofer +2
In the era of large-scale visual data, understanding collections of images is a challenging yet important task. To this end, we introduce ImageSet2Text, a novel method to automatic…
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
Lukas Aichberger, Kajetan Schweighofer, Sepp Hochreiter
Large Language Models (LLMs) are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To this end, reliable u…