Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio
Allen Roush, Sanjay Basu, Akshay Moorthy, Dmitry Dubovoy

TL;DR
This paper introduces a universal, plug-and-play technique for constraining language model outputs, demonstrated through an AI writing assistant that handles various lexical, semantic, and phonetic constraints without modifying the models.
Contribution
The authors propose a simple, model-agnostic method for applying constraints to language model outputs and develop an accessible AI writing tool called CTGS that supports diverse constraints.
Findings
Language models generate compelling text under constraints.
The method outperforms fine-tuning on a letter-omission dataset.
The approach is versatile and easy to implement.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
