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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20501 |
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| _version_ | 1866908610621079552 |
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| author | Balasubramanian, Rohan Gokulakrishnan, Nitish Saba, Syeda Jannatus Skiena, Steven |
| author_facet | Balasubramanian, Rohan Gokulakrishnan, Nitish Saba, Syeda Jannatus Skiena, Steven |
| contents | Lipograms are a unique form of constrained writing where all occurrences of a particular letter are excluded from the text, typified by the novel Gadsby, which daringly avoids all usage of the letter 'e'. In this study, we explore the power of modern large language models (LLMs) by transforming the novel F. Scott Fitzgerald's The Great Gatsby into a fully 'e'-less text. We experimented with a range of techniques, from baseline methods like synonym replacement to sophisticated generative models enhanced with beam search and named entity analysis. We show that excluding up to 3.6% of the most common letters (up to the letter 'u') had minimal impact on the text's meaning, although translation fidelity rapidly and predictably decays with stronger lipogram constraints. Our work highlights the surprising flexibility of English under strict constraints, revealing just how adaptable and creative language can be. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gatsby Without the 'E': Crafting Lipograms with LLMs Balasubramanian, Rohan Gokulakrishnan, Nitish Saba, Syeda Jannatus Skiena, Steven Computation and Language Lipograms are a unique form of constrained writing where all occurrences of a particular letter are excluded from the text, typified by the novel Gadsby, which daringly avoids all usage of the letter 'e'. In this study, we explore the power of modern large language models (LLMs) by transforming the novel F. Scott Fitzgerald's The Great Gatsby into a fully 'e'-less text. We experimented with a range of techniques, from baseline methods like synonym replacement to sophisticated generative models enhanced with beam search and named entity analysis. We show that excluding up to 3.6% of the most common letters (up to the letter 'u') had minimal impact on the text's meaning, although translation fidelity rapidly and predictably decays with stronger lipogram constraints. Our work highlights the surprising flexibility of English under strict constraints, revealing just how adaptable and creative language can be. |
| title | Gatsby Without the 'E': Crafting Lipograms with LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.20501 |