Framework teaches large language models to be safer and precise
Tutoring Large Language Models to be Domain-adaptive, Precise and Safe
Artificial IntelligenceComputational Engineering, Finance, and ScienceComputation and Language
Summary
Large language models sometimes make mistakes, say harmful things, or fail to respect different cultures and languages. The authors present a new way to teach these models to adapt better to specific subject areas, avoid making things up, and stop generating unsafe content in real time. They also focus on making the models sensitive to the cultural and language differences of people using them. This approach helps build AI tools that are more knowledgeable, careful, and respectful.
What this means in practice
- •For enterprise ai developers: Create AI assistants that provide accurate and reliable answers in specialized professional fields while preventing unsafe outputs during interactions.
- •For multilingual content moderators: Deploy safer AI that respects diverse language norms and cultural sensitivities in real-time content moderation across different languages and regions.
Authors
Somnath Banerjee
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.