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Previous state-of-the-art models for lexical simplification consist ofcomplex pipelines with several components, each of which requires deeptechnical knowledge and fine-tuned interaction to achieve its full potential.As an alternative, we describe a frustratingly simple pipeline based onprompted GPT-3 responses, beating competing approaches by a wide margin insettings with few training instances. Our best-performing submission to theEnglish language track of the TSAR-2022 shared task consists of an ``ensemble''of six different prompt templates with varying context levels. As alate-breaking result, we further detail a language transfer technique thatallows simplification in languages other than English. Applied to the Spanishand Portuguese subset, we achieve state-of-the-art results with only minormodification to the original prompts. Aside from detailing the implementationand setup, we spend the remainder of this work discussing the particularitiesof prompting and implications for future work. Code for the experiments isavailable online at https://github.com/dennlinger/TSAR-2022-Shared-Task

UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?