1 citations · 2 across the 9 of their papers we have counts for
10 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…
Is It Novel and Why? Fine-Grained Patent Novelty Prediction Based on Passage Retrieval
Valentin Knappich, Anna Hätty, Simon Razniewski +1
Novelty assessment is a critical yet complex task in the examination process for patent acceptance, requiring examiners to determine whether an invention is disclosed in a prior ar…
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…
PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claims
Valentin Knappich, Annemarie Friedrich, Anna Hätty +1
Patent claims define the scope of protection for an invention. If there are ambiguities in a claim, it is rejected by the patent office. In the US, this is referred to as indefinit…
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…
Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs
Valentin Knappich, Simon Razniewski, Anna Hätty +1
Dealing with long and highly complex technical text is a challenge for Large Language Models (LLMs), which still have to unfold their potential in supporting expensive and timeinte…
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports
Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati +4
Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically dist…
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…
BoschAI @ PLABA 2023: Leveraging Edit Operations in End-to-End Neural Sentence Simplification
Valentin Knappich, Simon Razniewski, Annemarie Friedrich
Automatic simplification can help laypeople to comprehend complex scientific text. Language models are frequently applied to this task by translating from complex to simple languag…
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…