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cs.CL2026
Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
Carolina Camassa, Derek Shiller
Language models are trained to follow instructions, but they are also powerful pattern completers. What happens when these two objectives conflict? We construct conversations in wh…
cs.CL2025
Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas
Giulia Iadisernia, Carolina Camassa
We evaluate whether persona-based prompting improves Large Language Model (LLM) performance on macroeconomic forecasting tasks. Using 2,368 economics-related personas from the Pers…
cs.CL2024
Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios
Camilla Bignotti, Carolina Camassa
In this paper, we conduct an empirical analysis of how large language models (LLMs), specifically GPT-4, interpret constitutional principles in complex decision-making scenarios. W…