mlsanityData to 14 Oct 2026

Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio

arXiv 2306.15926 ↗First posted 28 Jun 2023cs.CL cs.AI cs.LGcited by 9

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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

  1. 2022Classifier-Free Diffusion Guidancecited by 5,847
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  4. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  6. 2021Controlled Text Generation as Continuous Optimization with Multiple Constraintscited by 70
  7. 2022Mix and Match: Learning-free Controllable Text Generation using Energy Language Modelscited by 66
  8. 2021A Plug-and-Play Method for Controlled Text Generationcited by 56
  9. 2019Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialoguecited by 35
  10. 2021Constrained Text Generation with Global Guidance -- Case Study on CommonGencited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.

9 papers.