Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio
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Abstract (arXiv)
Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under significant constraints. We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called Constrained Text Generation Studio (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word. We introduce a novel dataset of prose that omits the letter e. We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset. We also present a Huggingface space web-app presenting this technique called Gadsby. The code is available to the public here: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 28 Jun 2023.
Builds on
10 of 14 references.
References · 10
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Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.
9 papers.
- 2024Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Modelscited by 10
- 2026What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answeringcited by 6
- 2023Constraints First: A New MDD-based Model to Generate Sentences Under Constraintscited by 4
- 2025Phraselette: A Poet's Procedural Palettecited by 4
- 2024Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrasescited by 3
- 2024Approximately Aligned Decodingcited by 3
- 2025Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognitioncited by 1
- 2025Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Modelscited by 1
- 2025Gatsby Without the 'E': Crafting Lipograms with LLMscited by 0
- 2026What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answering28 Jan 2026
- 2025Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models16 Oct 2025
- 2025Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition11 Jun 2025
- 2025Gatsby Without the 'E': Crafting Lipograms with LLMs26 May 2025
- 2025Phraselette: A Poet's Procedural Palette8 Mar 2025
- 2024Approximately Aligned Decoding1 Oct 2024
- 2024Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models23 May 2024
- 2024Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases20 Mar 2024
- 2023Constraints First: A New MDD-based Model to Generate Sentences Under Constraints21 Sep 2023