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arXiv · 2206.04812

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

Daphne Ippolito·Liam Dugan·Emily Reif·Ann Yuan·Andy Coenen·Chris Callison-Burch
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Abstract

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

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Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch. 2022-06-09. The Case for a Single Model that can Both Generate Continuations and Fill in the Blank. https://arxiv.org/abs/2206.04812

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