Can AI Outperform Human Experts in Creating Social Media Creatives?

• URL: http://arxiv.org/abs/2404.00018v1
• Date: Tue, 19 Mar 2024 07:41:45 GMT
• Title: Can AI Outperform Human Experts in Creating Social Media Creatives?
• Authors: Eunkyung Park, Raymond K. Wong, Junbum Kwon,
• Abstract summary: This paper evaluates AI's capability in the creative domain compared to human experts. We propose a novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by Large Language Models. We find that AI excels human experts, and Midjourney is better than the other text-to-image generators.
• Score: 0.6963971634605797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence has outperformed human experts in functional tasks such as chess and baduk. How about creative tasks? This paper evaluates AI's capability in the creative domain compared to human experts, which little research has been conducted so far. We propose a novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by Large Language Models. We take the most popular Instagram posts (with the biggest number of like clicks) in top brands' Instagram accounts to create social media creatives. We give GPT 4 several prompt instructions with text descriptions to generate the most effective prompts for cutting-edge text-to-image generators: Midjourney, DALL E 3, and Stable Diffusion. LLM-augmented prompts can boost AI's abilities by adding objectives, engagement strategy, lighting and brand consistency for social media image creation. We conduct an extensive human evaluation experiment, and find that AI excels human experts, and Midjourney is better than the other text-to-image generators. Surprisingly, unlike conventional wisdom in the social media industry, prompt instruction including eye-catching shows much poorer performance than those including natural. Regarding the type of creatives, AI improves creatives with animals or products but less with real people. Also, AI improves creatives with short text descriptions more than with long text descriptions, because there is more room for AI to augment prompts with shorter descriptions.

Related papers

• Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art [3.2727589223804507]
  This paper investigates whether concealed prompts sold on prompt marketplaces can be considered as secure intellectual property. Humans and AI tools may be able to approximately infer the prompts based on publicly advertised sample images. Our findings indicate that while humans and human-AI collaborations can infer prompts and generate similar images with high accuracy, they are not as successful as using the original prompt.
  arXiv  Detail & Related papers  (2024-10-10T22:41:13Z)
• AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text [53.15652021126663]
  We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text. To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm. Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2% higher than that of LLMs.
  arXiv  Detail & Related papers  (2024-10-05T18:55:01Z)
• Can AI Enhance its Creativity to Beat Humans ? [0.0]
  This study investigates the creative performance of artificial intelligence (AI) compared to humans. Human external evaluators have scored creative outputs generated by humans and AI. Results suggest that integrating human feedback is crucial for maximizing AI's creative potential.
  arXiv  Detail & Related papers  (2024-09-27T14:19:07Z)
• Can AI Be as Creative as Humans? [84.43873277557852]
  We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
  arXiv  Detail & Related papers  (2024-01-03T08:49:12Z)
• The Good, The Bad, and Why: Unveiling Emotions in Generative AI [73.94035652867618]
  We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
  arXiv  Detail & Related papers  (2023-12-18T11:19:45Z)
• ArchiGuesser -- AI Art Architecture Educational Game [0.5919433278490629]
  generative AI can create educational content from text, speech, to images based on simple input prompts. In this paper we present the multisensory educational game ArchiGuesser that combines various AI technologies to serve a single purpose.
  arXiv  Detail & Related papers  (2023-12-14T20:48:26Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity [1.332560004325655]
  We compare human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Our findings suggest that GAIs are valuable assistants in the creative process.
  arXiv  Detail & Related papers  (2023-03-21T16:35:01Z)
• Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI [8.379286663107845]
  Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. This paper presents the results of a qualitative survey investigating how creative professionals think about generative AI.
  arXiv  Detail & Related papers  (2023-03-15T20:57:03Z)
• Telling Creative Stories Using Generative Visual Aids [52.623545341588304]
  We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as story writing aid wrote significantly more creative, original, complete and visualizable stories. Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.
  arXiv  Detail & Related papers  (2021-10-27T23:13:47Z)
• The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
  The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
  arXiv  Detail & Related papers  (2021-07-05T12:18:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.