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

2026-09-19

AI Summary

本文提出一种框架，通过主动学习、图知识和实时对齐机制等方法，解决大语言模型在专业领域适应性、伦理安全及文化敏感性方面的问题。

Abstract

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.

Problem

• safety
• ethics
• cultural sensitivity
• domain adaptation
• hallucinations

Innovation

• responsible intelligence
• domain adaptation
• active learning
• graph-based knowledge
• decoding-time alignment