50 citations · 64 across the 5 of their papers we have counts for
5 papers
What are the limits to biomedical research acceleration through general-purpose AI?
Konstantin Hebenstreit, Constantin Convalexius, Stephan Reichl +3
Although general-purpose artificial intelligence (GPAI) is widely expected to accelerate scientific discovery, its practical limits in biomedicine remain unclear. We assess this po…
TRIAGE: Ethical Benchmarking of AI Models Through Mass Casualty Simulations
Nathalie Maria Kirch, Konstantin Hebenstreit, Matthias Samwald
We present the TRIAGE Benchmark, a novel machine ethics (ME) benchmark that tests LLMs' ability to make ethical decisions during mass casualty incidents. It uses real-world ethical…
A collection of principles for guiding and evaluating large language models
Konstantin Hebenstreit, Robert Praas, Matthias Samwald
Large language models (LLMs) demonstrate outstanding capabilities, but challenges remain regarding their ability to solve complex reasoning tasks, as well as their transparency, ro…
An automatically discovered chain-of-thought prompt generalizes to novel models and datasets
Konstantin Hebenstreit, Robert Praas, Louis P Kiesewetter +1
Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how re…
ThoughtSource: A central hub for large language model reasoning data
Simon Ott, Konstantin Hebenstreit, Valentin Liévin +6
Large language models (LLMs) such as GPT-4 have recently demonstrated impressive results across a wide range of tasks. LLMs are still limited, however, in that they frequently fail…