  The integration of emotional support into various conversational scenarios
presents profound societal benefits, such as social interactions, mental health
counseling, and customer service. However, there are unsolved challenges that
hinder real-world applications in this field, including limited data
availability and the absence of well-accepted model training paradigms. This
work endeavors to navigate these challenges by harnessing the capabilities of
Large Language Models (LLMs). We introduce an innovative methodology that
synthesizes human insights with the computational prowess of LLMs to curate an
extensive emotional support dialogue dataset. Our approach is initiated with a
meticulously designed set of dialogues spanning diverse scenarios as generative
seeds. By utilizing the in-context learning potential of ChatGPT, we
recursively generate an ExTensible Emotional Support dialogue dataset, named
ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model,
examining the impact of diverse training strategies, ultimately yielding an LLM
meticulously optimized for emotional support interactions. An exhaustive
assessment of the resultant model showcases its proficiency in offering
emotional support, marking a pivotal step in the realm of emotional support
bots and paving the way for subsequent research and implementations.
