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

arXiv: https://arxiv.org/abs/2609.23071 · HTML: https://mlsanity.com/paper/2609.23071/

## 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 19 Sep 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)

This thesis proposes a framework for "responsible intelligence" to address AI's critical challenges in safety, ethics, and cultural sensitivity. It advances three core areas: First, it improves domain adaptation in specialized fields using active learning and graph-based knowledge to reduce hallucinations. Second, it enhances ethical rigor via a novel decoding-time alignment mechanism that proactively blocks harmful text generation in real-time. Finally, it ensures cultural and multilingual safety through language-specific steering that respects diverse linguistic and social norms. Ultimately, this work provides a blueprint for building next-generation AI that is contextually knowledgeable, ethically sound, and culturally adaptable.