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This paper surveys the current landscape of poetry generation using large language models, examining both the technical capabilities and the aesthetic limitations of these systems. Through a series of experiments comparing LLM-generated verse with human-authored poetry across multiple dimensions (metaphor density, sonic patterning, emotional resonance), we investigate the boundaries of machine creativity and propose new evaluation frameworks for computational poetry.