Adaptive Instruction Tuning for Multi-Domain Dialogue Generation in Large Language Models

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, adapting LLMs to multi-domain dialogue systems remains challenging due to domain distribution shifts, instruction diversity, and varying conversational contexts. This paper proposes an adaptive instruction tuning framework that enables LLMs to dynamically adjust dialogue generation strategies across heterogeneous domains. The method integrates domain-aware instruction embeddings, reinforcement-based response optimization, and context-aware decoding mechanisms. The framework improves response relevance, coherence, and adaptability across domains such as healthcare, finance, and customer service. Experimental evaluations demonstrate that the proposed method outperforms baseline instruction-tuned models in both automatic metrics and human evaluation.