Probing the Embedding Space of Transformers via Minimal Token Perturbations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918067699712000 |
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| author | Conti, Eddie Astruc, Alejandro Parafita, Alvaro Brando, Axel |
| author_facet | Conti, Eddie Astruc, Alejandro Parafita, Alvaro Brando, Axel |
| contents | Understanding how information propagates through Transformer models is a key challenge for interpretability. In this work, we study the effects of minimal token perturbations on the embedding space. In our experiments, we analyze the frequency of which tokens yield to minimal shifts, highlighting that rare tokens usually lead to larger shifts. Moreover, we study how perturbations propagate across layers, demonstrating that input information is increasingly intermixed in deeper layers. Our findings validate the common assumption that the first layers of a model can be used as proxies for model explanations. Overall, this work introduces the combination of token perturbations and shifts on the embedding space as a powerful tool for model interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probing the Embedding Space of Transformers via Minimal Token Perturbations Conti, Eddie Astruc, Alejandro Parafita, Alvaro Brando, Axel Machine Learning Artificial Intelligence Understanding how information propagates through Transformer models is a key challenge for interpretability. In this work, we study the effects of minimal token perturbations on the embedding space. In our experiments, we analyze the frequency of which tokens yield to minimal shifts, highlighting that rare tokens usually lead to larger shifts. Moreover, we study how perturbations propagate across layers, demonstrating that input information is increasingly intermixed in deeper layers. Our findings validate the common assumption that the first layers of a model can be used as proxies for model explanations. Overall, this work introduces the combination of token perturbations and shifts on the embedding space as a powerful tool for model interpretability. |
| title | Probing the Embedding Space of Transformers via Minimal Token Perturbations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.18011 |