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Distribution linguistic preference relations (DLPRs) that model linguistic expressions with the aid of probabilistic distributions of multiple linguistic terms provide an effective tool to accurately elicit the preferences of decision makers (DMs) in linguistic decisions. Meanwhile, numerical scale models have been suitable choices for DMs to handle computing with words when solving linguistic decision problems. This study focuses on improving the group decision making (GDM) with DLPRs via the help of numerical scale models by filling the following gap. It is obvious that words might exhibit different meanings for different people. DMs may have a varying understanding of a given linguistic term in real-world fuzzy linguistic GDM. Setting personalized semantics of the linguistic terms for each DM becomes a critical task in GDM with DLPRs. To do this, we first define an improved numerical scale model to facilitate the …