Papers › CTRL: A Conditional Transformer Language Model for Controllable Generation
CTRL: A Conditional Transformer Language Model for Controllable Generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, Richard Socher
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
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Code
Syntology Ran 13 of 14 code samples harvested from 3 repositories linked to this paper; 1 has no recorded run. Of those that ran: 3 ran · honoured contract; 1 ran · violated contract; 3 ran · our draft was wrong; 6 ran with no contract checked.
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Code Syntology ran Syntology
14 samples harvested; 13 ran; 3 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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