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.

翻译：近期生成式人工智能的发展趋势聚焦于各类应用，包括创作故事、插画、诗歌、文章、计算机代码、音乐作品和视频。外在幻觉是此类生成式人工智能的关键局限，可能导致其在建立和维护可信度方面面临重大挑战。本文提出两个新概念，我们认为这将有助于研究界应对生成式人工智能模型应用中的相关局限。首先，我们提出生成式人工智能输出"可取性"的定义，以及影响此特性的三个可观测因素。其次，受Martin Fowler代码气味的启发，我们提出"提示气味"概念及其对生成式人工智能输出可取性产生的负面影响。我们期望本研究能推动关于生成式人工智能输出可取性的持续讨论，并以富有意义的方式促进该领域发展。