A data science team is using a state-of-the-art, highly robust language model for a critical sentiment analysis task. The model is known for its consistent and accurate outputs across a wide range of inputs. To further boost accuracy, the project lead proposes an ensembling strategy: for each piece of text, they will use 10 slightly different but semantically similar prompts (e.g., 'What is the sentiment of this text?', 'Is this review positive or negative?') and take the majority vote of the model's responses.

Critique this proposed strategy. Based on the characteristics of the model being used, evaluate the likelihood that this ensembling approach will provide a significant performance improvement and justify your reasoning.