  Accurately handling the underlying support values in sentences is crucial for
understanding the speaker's tendencies, yet it poses a challenging task in
natural language understanding (NLU). In this article, we explore the potential
of fine-tuning and prompt tuning in this downstream task, using the Human Value
Detection 2023. Additionally, we attempt to validate whether models can
effectively solve the problem based on the knowledge acquired during the
pre-training stage. Simultaneously, our interest lies in the capabilities of
large language models (LLMs) aligned with RLHF in this task, and some
preliminary attempts are presented.
