Probing the topology of the space of tokens with structured prompts
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915205371396096 |
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| author | Robinson, Michael Dey, Sourya Kushner, Taisa |
| author_facet | Robinson, Michael Dey, Sourya Kushner, Taisa |
| contents | This article presents a general and flexible method for prompting a large language model (LLM) to reveal its (hidden) token input embedding up to homeomorphism. Moreover, this article provides strong theoretical justification -- a mathematical proof for generic LLMs -- for why this method should be expected to work. With this method in hand, we demonstrate its effectiveness by recovering the token subspace of Llemma-7B. The results of this paper apply not only to LLMs but also to general nonlinear autoregressive processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probing the topology of the space of tokens with structured prompts Robinson, Michael Dey, Sourya Kushner, Taisa Differential Geometry Artificial Intelligence 53Z50, 58Z05 I.2.7 This article presents a general and flexible method for prompting a large language model (LLM) to reveal its (hidden) token input embedding up to homeomorphism. Moreover, this article provides strong theoretical justification -- a mathematical proof for generic LLMs -- for why this method should be expected to work. With this method in hand, we demonstrate its effectiveness by recovering the token subspace of Llemma-7B. The results of this paper apply not only to LLMs but also to general nonlinear autoregressive processes. |
| title | Probing the topology of the space of tokens with structured prompts |
| topic | Differential Geometry Artificial Intelligence 53Z50, 58Z05 I.2.7 |
| url | https://arxiv.org/abs/2503.15421 |