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

• URL: http://arxiv.org/abs/2311.13102v2
• Date: Thu, 18 Jul 2024 05:45:45 GMT
• Title: Detecting out-of-distribution text using topological features of transformer-based language models
• Authors: Andres Pollano, Anupam Chaudhuri, Anj Simmons,
• Abstract summary: We explore the use of topological features of self-attention maps from transformer-based language models to detect when input text is out of distribution. We evaluate our approach on BERT and compare it to a traditional OOD approach using CLS embeddings. Our results show that our approach outperforms CLS embeddings in distinguishing in-distribution samples from far-out-of-domain samples, but struggles with near or same-domain datasets.
• Score: 0.5735035463793009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To safeguard machine learning systems that operate on textual data against out-of-distribution (OOD) inputs that could cause unpredictable behaviour, we explore the use of topological features of self-attention maps from transformer-based language models to detect when input text is out of distribution. Self-attention forms the core of transformer-based language models, dynamically assigning vectors to words based on context, thus in theory our methodology is applicable to any transformer-based language model with multihead self-attention. We evaluate our approach on BERT and compare it to a traditional OOD approach using CLS embeddings. Our results show that our approach outperforms CLS embeddings in distinguishing in-distribution samples from far-out-of-domain samples, but struggles with near or same-domain datasets.

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