3 papers
cs.CL2024
MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks
John Francis, Saba Esnaashari, Anton Poletaev +3
Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We defi…
cs.CY2024
Mapping the individual, social, and biospheric impacts of Foundation Models
Andrés DomÃnguez Hernández, Shyam Krishna, Antonella Maia Perini +9
Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the…
cs.CY2024
'Team-in-the-loop': Ostrom's IAD framework 'rules in use' to map and measure contextual impacts of AI
Deborah Morgan, Youmna Hashem, John Francis +3
This article explores how the 'rules in use' from Ostrom's Institutional Analysis and Development Framework (IAD) can be developed as a context analysis approach for AI. AI risk as…