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A linguistic distribution assessment is an effective approach to represent uncertain preferences in large-scale group decision making (LSGDM). The same word often signifies different things for different decision makers in linguistic distribution assessments, which is called personalized individual semantics (PIS). Moreover, preference conflicts widely exist in LSGDM. This paper develops a new framework to address PIS and consensus in LSGDM using linguistic distribution preference relations (LDPRs). In the proposed LSGDM framework, a consistency-driven optimization model is put forward to produce the numerical scales with the PIS by maximizing the consistency of the additive preference relation that is transformed from its LDPR. Then, a preference clustering technique is employed to decompose decision makers into different clusters for managing their preferences. Next, the paper devises a two-stage …