so much depends / upon / a whitespace: Why Whitespace Matters for Poets and LLMs
Not on the map. Why?
Abstract (arXiv)
Whitespace is a critical component of poetic form, reflecting both adherence to standardized forms and rebellion against those forms. Each poem's whitespace distribution reflects the artistic choices of the poet and is an integral semantic and spatial feature of the poem. Yet, despite the popularity of poetry as both a long-standing art form and as a generation task for large language models (LLMs), whitespace has not received sufficient attention from the NLP community. Using a corpus of 19k English-language published poems from Poetry Foundation, we investigate how 4k poets have used whitespace in their works. We release a subset of 2.8k public-domain poems with preserved formatting to facilitate further research in this area. We compare whitespace usage in the published poems to (1) 51k LLM-generated poems, and (2) 12k unpublished poems posted in an online community. We also explore whitespace usage across time periods, poetic forms, and data sources. Additionally, we find that different text processing methods can result in significantly different representations of whitespace in poetry data, motivating us to use these poems and whitespace patterns to discuss implications for the processing strategies used to assemble pretraining datasets for LLMs.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 84 references that are in the map. First posted on arXiv 19 Oct 2025.
Builds on
19 of 84 references.
References · 19
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7 more
- 2024Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenizationcited by 32
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Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Riskscited by 3
- 2026The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corporacited by 0
- 2026Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media postscited by 0
- 2026Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Featurescited by 0
- 2026Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Features29 Aug 2026
- 2026Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts10 Aug 2026
- 2026The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora10 Aug 2026
- 2026Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks6 Mar 2026