Large Language Models (LLMs) are revolutionizing several areas of Artificial
Intelligence. One of the most remarkable applications is creative writing,
e.g., poetry or storytelling: the generated outputs are often of astonishing
quality. However, a natural question arise: can LLMs really be considered
creative? In this article we firstly analyze the development of LLMs under the
lens of creativity theories, investigating the key open questions and
challenges. Then, we identify a set of "easy" and "hard" problems in machine
creativity, discussing them in relation to LLMs. Finally, we analyze the
societal impact of these technologies with a particular focus on the creative
industries.

本文分析了大型语言模型在创造性写作方面的应用，讨论了其中的挑战，探讨了机器创造性中的一些问题，以及这些技术在创意产业中的社会影响。

关于大型语言模型的创新力

On the Creativity of Large Language Models

Measuring machine creativity is one of the most fascinating challenges in
Artificial Intelligence. This paper explores the possibility of using
generative learning techniques for automatic assessment of creativity. The
proposed solution does not involve human judgement, it is modular and of
general applicability. We introduce a new measure, namely DeepCreativity, based
on Margaret Boden's definition of creativity as composed by value, novelty and
surprise. We evaluate our methodology (and related measure) considering a case
study, i.e., the generation of 19th century American poetry, showing its
effectiveness and expressiveness.

本文提出使用生成式学习技术进行机器创造力自动评估、并在 19 世纪美国诗歌生成中表现出其有效性和表达力的新指标 DeepCreativity。

深层创造力：用深度学习技术测量创造力

DeepCreativity: Measuring Creativity with Deep Learning Techniques

Artificial intelligence (AI) is increasingly utilized in synthesizing
visuals, texts, and audio. These AI-based works, often derived from neural
networks, are entering the mainstream market, as digital paintings, songs,
books, and others. We conceptualize both existing and future human-in-the-loop
(HITL) approaches for creative applications and to develop more expressive,
nuanced, and multimodal models. Particularly, how can our expertise as curators
and collaborators be encoded in AI models in an interactive manner? We examine
and speculate on long term implications for models, interfaces, and machine
creativity. Our selection, creation, and interpretation of AI art inherently
contain our emotional responses, cultures, and contexts. Therefore, the
proposed HITL may help algorithms to learn creative processes that are much
harder to codify or quantify. We envision multimodal HITL processes, where
texts, visuals, sounds, and other information are coupled together, with
automated analysis of humans and environments. Overall, these HITL approaches
will increase interaction between human and AI, and thus help the future AI
systems to better understand our own creative and emotional processes.

该研究探讨了在艺术创作中将人类专业知识与 AI 模型相结合，以实现更具表现力、细腻和多模态的生成作品的方法，并提出 Human-in-the-Loop 的设计，以增加人类与 AI 之间的互动，从而帮助未来的 AI 系统更好地理解我们自己的创造和情感过程。