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It is standard procedure these days to solve Information Extraction task byfine-tuning large pre-trained language models. This is not the case forgeneration task, which relies on a variety of techniques for controlledlanguage generation. In this paper, we describe a system that fine-tunes anatural language generation model for the problem of solving Writer's Block.The fine-tuning changes the conditioning to also include the right context inaddition to the left context, as well as an optional list of entities, thesize, the genre and a summary of the paragraph that the human author wishes togenerate. Our proposed fine-tuning obtains excellent results, even with a smallnumber of epochs and a total cost of USD 150. The system can be accessed as aweb-service, and all the code is released. A video showcasing the interface andthe model is also available.

Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models