4 citations · 10 across the 5 of their papers we have counts for
6 papers
Quantifying Retriever-Generator Alignment in RAG with Local Explanations
Korbinian Randl, Guido Rocchietti, Aron Henriksson +3
Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground outputs in external documents. However, the interaction between these components…
SemEval-2025 Task 9: The Food Hazard Detection Challenge
Korbinian Randl, John Pavlopoulos, Aron Henriksson +2
In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text impli…
Evaluating the Reliability of Self-Explanations in Large Language Models
Korbinian Randl, John Pavlopoulos, Aron Henriksson +1
This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output. We evaluate two kinds of such self…
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Korbinian Randl, John Pavlopoulos, Aron Henriksson +1
Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be app…
A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data
Ali Beikmohammadi, Mohammad Hosein Hamian, Neda Khoeyniha +3
The rapid influx of data-driven models into the industrial sector has been facilitated by the proliferation of sensor technology, enabling the collection of vast quantities of data…
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
Zahra Kharazian, Tony Lindgren, Sindri Magnússon +2
Predicting failures and maintenance time in predictive maintenance is challenging due to the scarcity of comprehensive real-world datasets, and among those available, few are of ti…