5 citations · 8 across the 12 of their papers we have counts for
7 papers · 1 filter
RREDCoT: Segment-Level Reward Redistribution for Reasoning Models
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger +1
Recent advancements in reasoning language models have been driven by Reinforcement Learning (RL) fine-tuning. Most often, these rely on the Group Relative Policy Optimization (GRPO…
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…
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…
Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi +1
Applying a machine learning model for decision-making in the real world requires to distinguish what the model knows from what it does not. A critical factor in assessing the knowl…
Quantification of Uncertainty with Adversarial Models
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi +2
Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic u…