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To explore how humans can best leverage LLMs for writing and how interactingwith these models affects feelings of ownership and trust in the writingprocess, we compared common human-AI interaction types (e.g., guiding system,selecting from system outputs, post-editing outputs) in the context ofLLM-assisted news headline generation. While LLMs alone can generatesatisfactory news headlines, on average, human control is needed to fixundesirable model outputs. Of the interaction methods, guiding and selectingmodel output added the most benefit with the lowest cost (in time and effort).Further, AI assistance did not harm participants' perception of controlcompared to freeform editing.

Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation