1 citations · 2 across the 7 of their papers we have counts for
7 papers
Founder effects shape the evolutionary dynamics of multimodality in open LLM families
Manuel Cebrian
Large language model (LLM) families are improving rapidly, yet it remains unclear how quickly multimodal capabilities emerge and propagate within open families. Using the ModelBiom…
Emergent evaluation hubs in a decentralizing large language model ecosystem
Manuel Cebrian, Tomomi Kito, Raul Castro Fernandez
Large language models are proliferating, and so are the benchmarks that serve as their common yardsticks. We ask how the agglomeration patterns of these two layers compare: do they…
Mass-Scale Analysis of In-the-Wild Conversations Reveals Complexity Bounds on LLM Jailbreaking
Aldan Creo, Raul Castro Fernandez, Manuel Cebrian
As large language models (LLMs) become increasingly deployed, understanding the complexity and evolution of jailbreaking strategies is critical for AI safety. We present a mass-sca…
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed +23
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activitie…
Supervision policies can shape long-term risk management in general-purpose AI models
Manuel Cebrian, Emilia Gomez, David Fernandez Llorca
The rapid proliferation and deployment of General-Purpose AI (GPAI) models, including large language models (LLMs), present unprecedented challenges for AI supervisory entities. We…
Can adversarial attacks by large language models be attributed?
Manuel Cebrian, Andres Abeliuk, Jan Arne Telle
Attributing outputs from Large Language Models (LLMs) in adversarial settings-such as cyberattacks and disinformation campaigns-presents significant challenges that are likely to g…
Conversational Complexity for Assessing Risk in Large Language Models
John Burden, Manuel Cebrian, Jose Hernandez-Orallo
Large Language Models (LLMs) present a dual-use dilemma: they enable beneficial applications while harboring potential for harm, particularly through conversational interactions. D…