As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standards are elusive and judgments are inherently subjective. We conducted controlled experiments using Raymond Queneau's Exercises in Style (1947) to measure attribution bias across evaluators. Study 1 compared human participants (N=556) and AI models (N=13) evaluating literary passages from Queneau versus GPT-4-generated versions under three conditions: blind, accurately labeled, and counterfactually labeled. Study 2 tested bias generalization across a 14$\times$14 matrix of AI evaluators and creators. Both studies revealed systematic pro-human attribution bias. Humans showed +13.7 percentage point (pp) bias (Cohen's h = 0.28, 95% CI: 0.21-0.34), while AI models showed +34.3 percentage point bias (h = 0.70, 95% CI: 0.65-0.76), a 2.5-fold stronger effect (P$<$0.001). Study 2 confirmed this bias operates across AI architectures (+25.8pp, 95% CI: 24.1-27.6%), demonstrating that AI systems systematically devalue creative content when labeled as "AI-generated" regardless of which AI created it. We also find that attribution labels cause evaluators to invert assessment criteria, with identical features receiving opposing evaluations based solely on perceived authorship. This suggests AI models have absorbed human cultural biases against artificial creativity during training. Our study represents the first controlled comparison of attribution bias between human and artificial evaluators in aesthetic judgment, revealing that AI systems not only replicate but amplify this human tendency.

In 2025, human writers will reassert their worth. In recent years, the race for more and more content has been driven by technological and market imperatives such as search engine optimization, which serves neither the creator nor the consumer. Human needs and desires have been sidelined in favor of the attention economy and the drive for clicks. Hailed as a boon for freedom of expression, the early promise of the internet has failed us. Literature and journalism have been replaced by valueless "content," primarily aimed at filling web pages rather than informing or entertaining.

It has become routine to report research results where Large Language Models (LLMs) outperform average humans in a wide range of language-related tasks, and creative text writing is no exception. It seems natural, then, to raise the bid: Are LLMs ready to compete in creative writing skills with a top (rather than average) novelist? To provide an initial answer for this question, we have carried out a contest between Patricio Pron (an awarded novelist, considered one of the best of his generation) and GPT-4 (one of the top performing LLMs), in the spirit of AI-human duels such as DeepBlue vs Kasparov and AlphaGo vs Lee Sidol. We asked Pron and GPT-4 to provide thirty titles each, and then to write short stories for both their titles and their opponent's. Then, we prepared an evaluation rubric inspired by Boden's definition of creativity, and we collected 5,400 manual assessments provided by literature critics and scholars. The results of our experimentation indicate that LLMs are still far from challenging a top human creative writer, and that reaching such level of autonomous creative writing skills probably cannot be reached simply with larger language models.

This is a summary of the paper "A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing", which was published in Findings of EMNLP 2023. We evaluate a range of recent state-of-the-art, instruction-tuned large language models (LLMs) on an English creative writing task, and compare them to human writers. For this purpose, we use a specifically-tailored prompt (based on an epic combat between Ignatius J. Reilly, main character of John Kennedy Toole's "A Confederacy of Dunces", and a pterodactyl) to minimize the risk of training data leakage and force the models to be creative rather than reusing existing stories. The same prompt is presented to LLMs and human writers, and evaluation is performed by humans using a detailed rubric including various aspects like fluency, style, originality or humor. Results show that some state-of-the-art commercial LLMs match or slightly outperform our human writers in most of the evaluated dimensions. Open-source LLMs lag behind. Humans keep a close lead in originality, and only the top three LLMs can handle humor at human-like levels.

We evaluate a range of recent LLMs on English creative writing, a challenging and complex task that requires imagination, coherence, and style. We use a difficult, open-ended scenario chosen to avoid training data reuse: an epic narration of a single combat between Ignatius J. Reilly, the protagonist of the Pulitzer Prize-winning novel A Confederacy of Dunces (1980), and a pterodactyl, a prehistoric flying reptile. We ask several LLMs and humans to write such a story and conduct a human evalution involving various criteria such as fluency, coherence, originality, humor, and style. Our results show that some state-of-the-art commercial LLMs match or slightly outperform our writers in most dimensions; whereas open-source LLMs lag behind. Humans retain an edge in creativity, while humor shows a binary divide between LLMs that can handle it comparably to humans and those that fail at it. We discuss the implications and limitations of our study and suggest directions for future research.

ChatGPT is a publicly available chatbot that can quickly generate texts on given topics, but it is unknown whether the chatbot is really superior to human writers in all aspects of writing and whether its writing quality can be prominently improved on the basis of updating commands. Consequently, this study compared the writing performance on a narrative topic by ChatGPT and Chinese intermediate English (CIE) learners so as to reveal the chatbot's advantage and disadvantage in writing. The data were analyzed in terms of five discourse components using Coh-Metrix (a special instrument for analyzing language discourses), and the results revealed that ChatGPT performed better than human writers in narrativity, word concreteness, and referential cohesion, but worse in syntactic simplicity and deep cohesion in its initial version. After more revision commands were updated, while the resulting version was facilitated in syntactic simplicity, yet it is still lagged far behind CIE learners' writing in deep cohesion. In addition, the correlation analysis of the discourse components suggests that narrativity was correlated with referential cohesion in both ChatGPT and human writers, but the correlations varied within each group.

When the company OpenAI launched its new artificial intelligence program, ChatGPT, in late 2022, educators began to worry. ChatGPT could generate text that seemed like a human wrote it. How could teachers detect whether students were using language generated by an AI chatbot to cheat on a writing assignment? As a linguist who studies the effects of technology on how people read, write and think, I believe there are other, equally pressing concerns besides cheating. These include whether AI, more generally, threatens student writing skills, the value of writing as a process, and the importance of seeing writing as a vehicle for thinking.

Artificial intelligence (AI) has the potential to revolutionize the way copywriters and technical writers work. From generating content to editing and proofreading, AI can assist copywriters and technical writers in a variety of ways. AI-powered tools have the potential to make the work of copywriters and technical writers faster, more efficient, and more accurate. However, it is important to note that AI is not meant to replace human writers, but rather to assist them. AI-generated content may not be as creative or engaging as that written by a human, but it can still be helpful as a starting point or as a way to save time on research, editing, and optimization. In conclusion, AI can assist copywriters and technical writers in many ways.

Artificial Intelligence has been promising for a long time to disrupt almost any industry that is knowledge based and relies on data and information. One platform is now starting to deliver. Open AI was founded in 2015 by Elon Musk and is expected to be valued at $29 Billion at its next round of funding. Open AI's latest technology tool, ChatGPT was released on November 29, 2022. In just one week one million users registered with the platform.

In less than 6 weeks' time, ChatGPT has taken the world by storm. If you haven't heard of ChatGPT – specifically ChatGPT3 – at this point, here's the low-down: It's a question-and-answer computer program which uses artificial intelligence and machine learning to create not only intelligent answers but writes it in a "human-like way." It's really quite astonishing how well it answers questions, and for that reason, it's already changed the world. That's why OpenAI's ChatGPT is valued at $29 billion, according to the Washington Post. Per the Post's reporting, OpenAI is in talks with investors currently and looks to be valued at nearly $30 billion.