5 citations · 8 across the 12 of their papers we have counts for
3 papers · 1 filter
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…
On Information-Theoretic Measures of Predictive Uncertainty
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi +1
Reliable estimation of predictive uncertainty is crucial for machine learning applications, particularly in high-stakes scenarios where hedging against risks is essential. Despite…
Improving Uncertainty Estimation through Semantically Diverse Language Generation
Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi +1
Large language models (LLMs) can suffer from hallucinations when generating text. These hallucinations impede various applications in society and industry by making LLMs untrustwor…