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Style representation learning builds content-independent representations ofauthor style in text. Stylometry, the analysis of style in text, is oftenperformed by expert forensic linguists and no large dataset of stylometricannotations exists for training. Current style representation learning usesneural methods to disentangle style from content to create style vectors,however, these approaches result in uninterpretable representations,complicating their usage in downstream applications like authorship attributionwhere auditing and explainability is critical. In this work, we use promptingto perform stylometry on a large number of texts to create a synthetic datasetand train human-interpretable style representations we call LISA embeddings. Werelease our synthetic stylometry dataset and our interpretable style models asresources.

Learning Interpretable Style Embeddings via Prompting LLMs