Knowledge-Aligned Domain Shift Tuning for Efficient Adaptation in Large Language Models

This paper introduces the Lottery Hedge Fund Hypothesis (LHFH) and proposes Knowledge-Aligned Domain Adaptation (KADA), a novel framework for addressing domain discrepancies between source and target domains. While LLMs demonstrate strong generalization across tasks, their performance often degrades on domain-specific tasks requiring specialized knowledge due to a mismatch in domain-specific knowledge distributions.