6 papers
The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations
Timo Pierre Schrader, Annemarie Friedrich, Simon Razniewski +1
Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While many works focus on extracti…
A Solver-in-the-Loop Framework for Improving LLMs on Answer Set Programming for Logic Puzzle Solving
Timo Pierre Schrader, Lukas Lange, Tobias Kaminski +2
The rise of large language models (LLMs) has sparked interest in coding assistants. While general-purpose programming languages are well supported, generating code for domain-speci…
QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios
Timo Pierre Schrader, Lukas Lange, Simon Razniewski +1
Reasoning is key to many decision making processes. It requires consolidating a set of rule-like premises that are often associated with degrees of uncertainty and observations to…
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
Timo Pierre Schrader, Simon Razniewski, Lukas Lange +1
Understanding causality is a core aspect of intelligence. The Event Causality Identification with Causal News Corpus Shared Task addresses two aspects of this challenge: Subtask 1…
MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain
Timo Pierre Schrader, Matteo Finco, Stefan Grünewald +2
Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extrac…
MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain
Timo Pierre Schrader, Teresa Bürkle, Sophie Henning +6
Scientific publications follow conventionalized rhetorical structures. Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence states a Motivation, a Result o…