238 citations · 498 across the 6 of their papers we have counts for
6 papers
Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
Thilo Hagendorff, Sarah Fabi
Large language models (LLMs) can perform reasoning computations both internally within their latent space and externally by generating explicit token sequences like chains of thoug…
Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4
Thilo Hagendorff, Sarah Fabi
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate…
Methodological reflections for AI alignment research using human feedback
Thilo Hagendorff, Sarah Fabi
The field of artificial intelligence (AI) alignment aims to investigate whether AI technologies align with human interests and values and function in a safe and ethical manner. AI…
Thinking Fast and Slow in Large Language Models
Thilo Hagendorff, Sarah Fabi, Michal Kosinski
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate…
Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
Sarah Fabi, Thilo Hagendorff
This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex…
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
Sarah Fabi, Sebastian Otte, Jonas Gregor Wiese +1
When comparing human with artificial intelligence, one major difference is apparent: Humans can generalize very broadly from sparse data sets because they are able to recombine and…