STEB

STEB: A comprehensive benchmark for evaluating style text embeddings across 96 datasets

Researchers introduce the Style Text Embedding Benchmark (STEB), an open-source evaluation suite covering 96 datasets across 7 languages to standardize assessment of style embeddings. STEB spans tasks including authorship verification, authorship retrieval, and AI-text detection. Key findings show that semantic embeddings consistently fail on stylistic tasks, and no single style embedding model dominates across all tasks.