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We attempt to detect out-of-distribution (OOD) text samples though applyingTopological Data Analysis (TDA) to attention maps in transformer-based languagemodels. We evaluate our proposed TDA-based approach for out-of-distributiondetection on BERT, a transformer-based language model, and compare the to amore traditional OOD approach based on BERT CLS embeddings. We found that ourTDA approach outperforms the CLS embedding approach at distinguishingin-distribution data (politics and entertainment news articles from HuffPost)from far out-of-domain samples (IMDB reviews), but its effectivenessdeteriorates with near out-of-domain (CNN/Dailymail) or same-domain (businessnews articles from HuffPost) datasets.

Detecting out-of-distribution text using topological features of transformer-based language models