Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces
arxiv(2023)
摘要
Large language models (LLMs) exhibit dynamic capabilities and appear to
comprehend complex and ambiguous natural language prompts. However, calibrating
LLM interactions is challenging for interface designers and end-users alike. A
central issue is our limited grasp of how human cognitive processes begin with
a goal and form intentions for executing actions, a blindspot even in
established interaction models such as Norman's gulfs of execution and
evaluation. To address this gap, we theorize how end-users 'envision'
translating their goals into clear intentions and craft prompts to obtain the
desired LLM response. We define a process of Envisioning by highlighting three
misalignments: (1) knowing whether LLMs can accomplish the task, (2) how to
instruct the LLM to do the task, and (3) how to evaluate the success of the
LLM's output in meeting the goal. Finally, we make recommendations to narrow
the envisioning gulf in human-LLM interactions.
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