LARGE LANGUAGE MODEL ALIGNMENT AND SAFETY: A REINFORCEMENT LEARNING FROM HUMAN FEEDBACK FRAMEWORK FOR REDUCING HALLUCINATION, BIAS, AND HARMFUL OUTPUT IN DOMAIN-SPECIFIC LLMS

Abstract

The deployment of Large Language Models (LLMs) in high-stakes domain-specific applications encompassing medical diagnosis support, legal document analysis, financial advisory systems, and educational assessment platforms has surfaced critical safety challenges that general-purpose alignment techniques inadequately address. Domain-specific LLMs exhibit three characteristic failure modes with severe real-world consequences: hallucination of domain-specific facts including fabricated medical dosages, non-existent legal precedents, and incorrect financial regulations; systematic bias reflecting training data skewness including gender bias in medical recommendations and racial bias in legal risk assessments; and harmful output generation violating domain-specific ethical standards. This paper presents SafeAlign-LLM, a novel multi-phase Reinforcement Learning from Human Feedback (RLHF) framework specifically architected for domain-specific LLM alignment, integrating four complementary techniques: supervised fine-tuning on domain-curated demonstration datasets, multi-dimensional reward modeling capturing helpfulness, harmlessness, honesty, and domain accuracy simultaneously, Proximal Policy Optimization (PPO) with KL-divergence constraint for stable policy training, and a Constitutional AI (CAI) layer enforcing domain-specific ethical principles through self-critique and revision. SafeAlign-LLM introduces three novel contributions beyond standard RLHF: a Hallucination Detection and Grounding (HDG) module using Retrieval-Augmented Generation cross-referencing against authoritative domain knowledge bases achieving 94.2% hallucination detection accuracy; a Multi-Dimensional Bias Auditing (MDBA) framework evaluating 12 bias dimensions across demographic axes; and an Uncertainty Quantification (UQ) mechanism enabling calibrated confidence expression. Evaluated across four domain-specific deployments including MedAlign-LLM, LegalAlign-LLM, FinAlign-LLM, and EduAlign-LLM, SafeAlign-LLM reduces hallucination rates by 73.4%, bias scores by 68.2%, and harmful output incidence by 89.7% compared to base RLHF, while maintaining 94.3% domain task performance, establishing a new state-of-the-art in safe domain-specific LLM deployment.