# Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe

arXiv: https://arxiv.org/abs/2602.13860 · HTML: https://mlsanity.com/paper/2602.13860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations](https://mlsanity.com/paper/2406.11801/) (2024)
- [How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries](https://mlsanity.com/paper/2402.15302/) (2024)
- [SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models](https://mlsanity.com/paper/2406.12274/) (2024)
- [Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance](https://mlsanity.com/paper/2406.11139/) (2024)
- [Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models](https://mlsanity.com/paper/2410.12880/) (2024)

## Abstract (arXiv)

The overarching research direction of this work is the development of a ''Responsible Intelligence'' framework designed to reconcile the immense generative power of Large Language Models (LLMs) with the stringent requirements of real-world deployment. As these models become a transformative force in artificial intelligence, there is an urgent need to move beyond general-purpose architectures toward systems that are contextually aware, inherently safer, and deeply respectful of global cultural nuances. This research navigates three interconnected threads: domain adaptation to ensure technical precision, ethical rigor to mitigate adversarial vulnerabilities, and cultural/multilingual alignment to promote global inclusivity. The methodological trajectory moves from classical supervised adaptation for task-specific demands to decoding-time alignment for safety, finally leveraging human feedback and preference modeling to achieve sociolinguistic acuity.