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

2026-02-14

AI Summary

This work addresses the persistent challenges faced by large language models in real-world deployment—namely, limited domain adaptability, insufficient safety guarantees, and inadequate cross-cultural inclusivity—which often hinder their ability to simultaneously achieve technical accuracy, ethical compliance, and cultural sensitivity. To bridge this gap, the paper introduces a novel “Responsible Intelligence” framework that unifies domain adaptation, safety alignment, and multilingual cultural adaptation within a cohesive co-optimization architecture. By integrating supervised fine-tuning, decoding-time constraints, reinforcement learning from human feedback (RLHF), and preference modeling, the proposed approach concurrently enhances model performance in specialized domains, robustness against adversarial attacks, and linguistic appropriateness and inclusivity across diverse cultural contexts.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• domain adaptation
• precision
• safety
• cultural alignment
• responsible intelligence

Innovation

• Responsible Intelligence
• Domain Adaptation
• Decoding-time Alignment
• Human Feedback
• Multilingual Alignment