The impressive capacity shown by recent text-to-image diffusion models to
generate high-quality pictures from textual input prompts has leveraged the
debate about the very definition of art. Nonetheless, these models have been
trained using text data collected from content-based labelling protocols that
focus on describing the items and actions in an image but neglect any
subjective appraisal. Consequently, these automatic systems need rigorous
descriptions of the elements and the pictorial style of the image to be
generated, otherwise failing to deliver. As potential indicators of the actual
artistic capabilities of current generative models, we characterise the
sentimentality, objectiveness and degree of abstraction of publicly available
text data used to train current text-to-image diffusion models. Considering the
sharp difference observed between their language style and that typically
employed in artistic contexts, we suggest generative models should incorporate
additional sources of subjective information in their training in order to
overcome (or at least to alleviate) some of their current limitations, thus
effectively unleashing a truly artistic and creative generation.

当前的文本到图像扩散模型表现出的卓越能力促进了关于艺术定义的讨论，然而，这些模型使用的文本数据缺乏主观评价，因此需要对元素和图片风格进行严格描述，建议在培训过程中加入主观信息，以发挥其真正的艺术创造力。

语言不仅仅是描述：论文本到图像模型中缺乏的比喻性语言

Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Models

Our lives are ruled by events of varying importance ranging from simple
everyday occurrences to incidents of societal dimension. And a lot of effort is
taken to exchange information and discuss about such events: generally
speaking, stringent narratives are formed to reduce complexity. But when
considering complex events like the current conflict between Russia and Ukraine
it is easy to see that those events cannot be grasped by objective facts alone,
like the start of the conflict or respective troop sizes. There are different
viewpoints and assessments to consider, a different understanding of the roles
taken by individual participants, etc. So how can such subjective and
viewpoint-dependent information be effectively represented together with all
objective information? Recently event-centric knowledge graphs have been
proposed for objective event representation in the otherwise primarily
entity-centric domain of knowledge graphs. In this paper we introduce a novel
and lightweight structure for event-centric knowledge graphs, which for the
first time allows for queries incorporating viewpoint-dependent and narrative
aspects. Our experiments prove the effective incorporation of subjective
attributions for event participants and show the benefits of specifically
tailored indexes for narrative query processing.

本文提出了一种新颖而轻量级的事件中心知识图谱结构，允许查询包括视角依赖和叙事方面，并证明了主观归属的有效结合，展示了为叙事查询处理专门定制的索引的好处。