LLMs as Writing Assistants: Exploring Perspectives on Sense of Ownership and Reasoning
This addresses the cognitive and ethical challenges in human-computer interaction for writing aid systems, but it is incremental as it builds on existing research on ownership and AI assistance.
The study investigated how using LLMs as writing assistants affects people's sense of ownership and reasoning, finding that users tend to credit LLMs more in creative tasks despite equal capabilities, and often claim authorship without full ownership.
Sense of ownership in writing confines our investment of thoughts, time, and contribution, leading to attachment to the output. However, using writing assistants introduces a mental dilemma, as some content isn't directly our creation. For instance, we tend to credit Large Language Models (LLMs) more in creative tasks, even though all tasks are equal for them. Additionally, while we may not claim complete ownership of LLM-generated content, we freely claim authorship. We conduct a short survey to examine these issues and understand underlying cognitive processes in order to gain a better knowledge of human-computer interaction in writing and improve writing aid systems.