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Imagine a writer is working on a story. At any given stage, they need to make a decision about what comes next in order to continue it. Complex cognitive processes and sociocultural factors inform this decision making. Integrating language models can help users write, but also add a cost in evaluating and incorporating their suggestions. In particular, there is an opaque process of integrating suggested continuations offered by a language model. We want to bring this out and to examine it. In this work, we ask questions like what kinds of processes are at play in suggestion integration. We study this by developing an AI-based writing assistant and studying it in a think-aloud creative writing study. Our system takes the text to user has already written, passes it through two fine-tuned GPT-2 model variants producing two distinct textual suggestions. We then extract keywords from these and pool them to query image and auditory databases to offer a diverse, multi-modal palette of suggest
