DMAP: A Distribution Map for Text

• URL: http://arxiv.org/abs/2602.11871v1
• Date: Thu, 12 Feb 2026 12:21:24 GMT
• Title: DMAP: A Distribution Map for Text
• Abstract summary: We present DMAP, a method that maps a text, via a language model, to a set of samples in the unit interval that jointly encode rank and probability information.<n>This representation enables efficient, model-agnostic analysis and supports a range of applications.
• Score: 5.2660301576695545
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offering a wealth of information. Extracting this signal typically relies on metrics such as perplexity, which do not adequately account for context; how one should interpret a given next-token probability is dependent on the number of reasonable choices encoded by the shape of the conditional distribution. In this work, we present DMAP, a mathematically grounded method that maps a text, via a language model, to a set of samples in the unit interval that jointly encode rank and probability information. This representation enables efficient, model-agnostic analysis and supports a range of applications. We illustrate its utility through three case studies: (i) validation of generation parameters to ensure data integrity, (ii) examining the role of probability curvature in machine-generated text detection, and (iii) a forensic analysis revealing statistical fingerprints left in downstream models that have been subject to post-training on synthetic data. Our results demonstrate that DMAP offers a unified statistical view of text that is simple to compute on consumer hardware, widely applicable, and provides a foundation for further research into text analysis with LLMs.

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