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The task of inserting text into a specified position in a passage, known asfill in the blank (FitB), is useful for a variety of applications where writersinteract with a natural language generation (NLG) system to craft text. Whileprevious work has tackled this problem with models trained specifically to dothe fill-in-the-blank task, a more useful model is one that can effectivelyperform _both_ FitB and continuation. In this work, we evaluate the feasibilityof using a single model to do both tasks. We show that models pre-trained witha FitB-style objective are capable of both tasks, while models pre-trained forcontinuation are not. Finally, we show how FitB models can be easily finetunedto allow for fine-grained control over the length and word choice of thegeneration.

The Case for a Single Model that can Both Generate Continuations and Fill in the Blank