30 citations · 94 across the 30 of their papers we have counts for
7 papers · 1 filter
Beyond the Hivemind: Escaping LLM Homogeneity via Meta-Persona Anchoring and Sequential Temperature Scaling
Tairan Fu, Javier Conde, Carlos Arriaga +3
Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questio…
Beyond Reproducibility: Token Probabilities Expose Large Language Model Nondeterminism
Tairan Fu, Gonzalo Martínez, Javier Conde +4
The execution of Large Language Models (LLMs) has been shown to produce nondeterministic results when run on Graphics Processing Units (GPUs), even when they are configured to prod…
Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities
Tairan Fu, David Campo-Nazareno, Javier Coronado-Blázquez +3
Large Language Models (LLMs) are capable of solving complex math problems or answer difficult questions on almost any topic, but can they generate random street addresses for Europ…
The Generative Energy Arena (GEA): Incorporating Energy Awareness in Large Language Model (LLM) Human Evaluations
Carlos Arriaga, Gonzalo Martínez, Eneko Sendin +2
The evaluation of large language models is a complex task, in which several approaches have been proposed. The most common is the use of automated benchmarks in which LLMs have to…
Real-time Spatial Retrieval Augmented Generation for Urban Environments
David Nazareno Campo, Javier Conde, Álvaro Alonso +3
The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundatio…
Understanding the Impact of Artificial Intelligence in Academic Writing: Metadata to the Rescue
Javier Conde, Pedro Reviriego, Joaquín Salvachúa +3
This column advocates for including artificial intelligence (AI)-specific metadata on those academic papers that are written with the help of AI in an attempt to analyze the use of…
Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Language Models
Jinhua Zhu, Javier Conde, Zhen Gao +3
The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thu…