Language models that generate texts on the basis of probabilities are best approached with solid scepticism with regard to factual accuracy, and with a little humour. Jack Krawczyk, who is responsible for the development of the chatbot “Bard” at Google, openly admitted in March 2023: “Bard and ChatGPT are large language models, not knowledge models. They are great at generating human-sounding text, they are not good at ensuring their text is fact-based.” Calling a language model with a wry wink “bard” hits the nail on the head: bards write poetry, tell stories and don’t necessarily stick to the truth, as we know since Plato.

Creating texts, especially literary texts, was previously the preserve of humans. Large Language Models (LLMS), however, are surprisingly good at identifying and replicating literary styles and genres. So how can we imagine literary text production from now on? Terms such as “consciousness”, “memory”, “intentionality” and “creativity” are surprisingly poorly defined, both for humans and for machines. With regard to the latter, the British cognitive scientist Margaret A. Boden has already dealt with the differences between human and machine creativity in her book “The Creative Mind” – emphasising that machines only appear to be creative to a certain degree. She distinguishes between three forms of creativity: a) making unfamiliar combinations of familiar ideas; b) explorative creativity; and c) transformative creativity.

Producing unknown combinations from known ideas is certainly what LLMs are good at, because that is how they are built: calculating the most likely recombinations from the data available, following the patterns present in the data. It should therefore no longer be a great challenge for an LLM to fabricate a short story in 99 different styles and thus replicate Raymond Queneau’s famous “Exercises de Style“. Literary variations such as permutations, rhyme forms, jargons, narrative perspectives, sociolects, etc. should be producible with a single prompt. The phrase “a single prompt” reveals the vagueness of the term “intention”: A human must enter the prompt and thus act “intentionally”; the machine takes care of everything else.

The second form of creativity, according to Boden, explores conceptual spaces, which we can imagine as established genres in the field of literature. Genres follow rules that outline the space in which the literary action takes place; sociologist Pierre Bourdieu described them as the “rules of art”. Not everything is possible in every genre: while in crime fiction the dead do not come back to life or move around as living corpses, this is certainly possible in fantasy or horror literature. LLMs are able to identify such spaces of possibilities and replicate the patterns that characterise them. Especially when the underlying data contains many examples of literary genres such as historical novels, fantasy and romance novels along with their characteristic styles and topoi, LLMs can reliably produce recombinations and thus explore the conceptual space. Since these spaces offer many possibilities, not all of which are equally attractive to human readers, we can think of these combinatorial explorations as human-machine collaborations: A human develops a sketch of a novel and lets the outlined plot be formulated chapter by chapter by the machine. Such collaborations can be criticised from an economic rather than from an aesthetic perspective: In order to know the current space of possibilities, LLMs must also have access to material that is under copyright. When it comes to systems like ChatGPT, the data basis of which is not disclosed, this amounts to a privatisation of culture that was once public. And, to use an old argument: Here, human labour is being replaced by a machinery that enables the relevant companies to skim off the generated surplus.

The third form of creativity described by Margaret Boden aims to transform the conceptual space. Here, the rules that describe this space are broken and new ones are established. We can think, for example, of Marcel Duchamp’s urinal entitled “Fountain“, Picasso’s first cubist painting “Les Demoiselles d’Avignon” or Italo Calvino’s “Le città invisibili“. However, in order to redesign the conceptual space, you first have to know it and be able to name the rules that characterise it in order to be able to realise such a transformative work in collaboration with a machine. A LLM cannot achieve this, as such models do not reflect their own activity, lack generalisable word knowledge, and their heuristics are geared towards identifying patterns, but not towards creating new ones. This is where human and machine creativity separate: human creativity has knowledge of the world and a (possibly intuitive) knowledge of the rules of a conceptual space; in a movement of departure from known concepts new solutions are found, radical ideas are developed and new rules are established. Transformative creativity enables humans to create new works in collaboration with a machine; however, the intention to leave the known space of possibilities seems to be  (still) reserved for humans.

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