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cs.LG2024
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…
cs.LG2024
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…