Reprogramming Open-Weight LLMs for Proactive, Socratic Behavior.

Researchers have explored the "cognitive plasticity" of open-weight large language models (LLMs), aiming to reprogram their default behavior from passive assistants to proactive, Socratic conversational agents. This involved a rigorous empirical investigation into how effectively these models can be modified to generate high-frequency questions under strict computational constraints. The study utilized a massive parallelized hyperparameter sweep, comprising 405 high-performance computing jobs, to precisely define the mathematical bounds for parameter-efficient fine-tuning (PEFT). Key findings include identifying an architectural threshold at LoRA rank r=16 and an optimal training window of 2 to 3 epochs for generalization capacity, achieving a minimum validation loss of 0.919. Scaling model capacity to 14 billion parameters further reduced localized evaluation perplexity to 1.414. The research also successfully decoupled assertive behavior from localized syntax using Direct Preference Optimization (DPO) and demonstrated robust zero-shot persona transfer across closely related linguistic families, while also identifying degradation pathways in morphologically distant languages. These results establish a strong empirical framework for compute-efficient, cross-lingual behavioral modification.