Prompt Smells: An Omen for Undesirable Generative AI Outputs
This work addresses trustworthiness issues in Generative AI for researchers and practitioners, but it is incremental as it builds on existing concepts like code smells without presenting new methods or data.
The paper tackles the problem of undesirable outputs from Generative AI, such as extrinsic hallucinations, by proposing a definition for output desirability and introducing the concept of 'prompt smells' to identify factors that negatively affect it.
Recent Generative Artificial Intelligence (GenAI) trends focus on various applications, including creating stories, illustrations, poems, articles, computer code, music compositions, and videos. Extrinsic hallucinations are a critical limitation of such GenAI, which can lead to significant challenges in achieving and maintaining the trustworthiness of GenAI. In this paper, we propose two new concepts that we believe will aid the research community in addressing limitations associated with the application of GenAI models. First, we propose a definition for the "desirability" of GenAI outputs and three factors which are observed to influence it. Second, drawing inspiration from Martin Fowler's code smells, we propose the concept of "prompt smells" and the adverse effects they are observed to have on the desirability of GenAI outputs. We expect our work will contribute to the ongoing conversation about the desirability of GenAI outputs and help advance the field in a meaningful way.