Viktor Schlegel

Can Transformers Reason in Fragments of Natural Language?

2022 · arXiv (Cornell University)

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally …

Representation Learning of Structured Data for Medical Foundation Models

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records like ICD-10 or SNOMED-CT, …

Learning to generate and evaluate fact-checking explanations with transformers

2024 · Engineering Applications of Artificial Intelligence

In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial …