89 citations · 91 across the 7 of their papers we have counts for
7 papers
Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models
Moritz Laber, Zohair Shafi, Germans Savcisens +6
Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem correctly, whereas efficien…
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models: A Controlled Study
Germans Savcisens, Samantha Dies, Courtney Maynard +1
Large language models (LLMs) are increasingly deployed in multi-agent environments. However, the processes by which beliefs form and propagate among interacting LLMs remain poorly…
Epistemic Familiarity is Associated With Belief Stability in Large Language Models
Samantha Dies, Courtney Maynard, Germans Savcisens +1
Large language models (LLMs) are widely used as information sources, yet small changes in semantic assumptions can destabilize their beliefs. We introduce P-StaT (Perturbation Stab…
The Trilemma of Truth in Large Language Models
Germans Savcisens, Tina Eliassi-Rad
The public often attributes human-like qualities to large language models (LLMs), assuming that they "know" certain things. In reality, LLMs encode information retained during trai…
REGE: A Method for Incorporating Uncertainty in Graph Embeddings
Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad
Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that…
Using Sequences of Life-events to Predict Human Lives
Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen +5
Over the past decade, machine learning has revolutionized computers' ability to analyze text through flexible computational models. Due to their structural similarity to written la…