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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.13102 |
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| _version_ | 1866929425545691136 |
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| author | Pollano, Andres Chaudhuri, Anupam Simmons, Anj |
| author_facet | Pollano, Andres Chaudhuri, Anupam Simmons, Anj |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13102 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Detecting out-of-distribution text using topological features of transformer-based language models Pollano, Andres Chaudhuri, Anupam Simmons, Anj Computation and Language Machine Learning Algebraic Topology 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. |
| title | Detecting out-of-distribution text using topological features of transformer-based language models |
| topic | Computation and Language Machine Learning Algebraic Topology |
| url | https://arxiv.org/abs/2311.13102 |