AI as a Tool for Simulation-Based Experiments in Literary Studies

• URL: http://arxiv.org/abs/2606.02293v1
• Date: Mon, 01 Jun 2026 14:16:13 GMT
• Title: AI as a Tool for Simulation-Based Experiments in Literary Studies
• Abstract summary: Generative artificial intelligence (AI) systems open new possibilities for experimentation in literary studies.<n>Current systems have not yet been shown to produce high-quality, book-length narrative texts.<n>We present the results of experiments on literary text generation, including comparisons to high-status, human-authored novels.
• Score: 1.455240131708017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence (AI) systems open new possibilities for experimentation in literary studies via controlled, grounded, large-scale, low-cost simulations of cultural production. Current systems have not yet been shown to produce high-quality, book-length narrative texts that reliably reflect arbitrarily specified cultural constraints or stylistic features. But there exists substantial relevant research on each of the components required for literary-historical simulation. These include the use and validation of AI systems as proxies for differentiable human populations; the narrative and stylistic properties of AI-generated texts; the stability and coherence of multiagent, multiturn AI simulations of human actors; and technical methods through which to alter in predictable ways the knowledge and behavior of generative systems. Together, these areas could provide a starting point for more ambitious AI-based modeling of cultural systems of literary production. We describe the possibilities and challenges of simulation-based experiments in literary studies, summarize the current state of the art in relevant fields, and explain key technical aspects of the work. To provide an example directly relevant to literary scholars, we present the results of experiments on literary text generation, including comparisons to high-status, human-authored novels. Our results include the first demonstration of (limited) in-distribution outputs by AI models in this domain. We conclude with a description of future work on full counterfactual literary-historical simulations using AI.

Related papers

• Artificial Intelligence and Modeling & Simulation: An Overview [2.982218441172364]
  Artificial intelligence (AI) and Modeling & Simulation (M&S) are increasingly intertwined.<n>This report provides a structured overview of the intersections of AI and M&S.
  arXiv  Detail & Related papers  (2026-08-01T00:36:44Z)
• The Art of Generative Narrativity [0.0]
  generative AI leads to experiments with non-verbal forms that have the potential to incite narratives through the audience's experience.<n>In five central sections, we discuss interrelated exemplars whose conceptual frameworks anticipate or underscore the issues of contemporary linguistic automation.<n>In closing sections, we summarize the expressive features of these exemplars and underline their value for critically assessing generative AI's cultural influence and fallouts.
  arXiv  Detail & Related papers  (2026-03-01T12:58:24Z)
• Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective [54.152639172274]
  Large-scale generative AI techniques have the potential to enhance data storytelling with their power in visual and narration generation.<n>We compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling.<n>The benefits of these AI techniques and implications to human-AI collaboration are also revealed.
  arXiv  Detail & Related papers  (2025-03-04T13:56:18Z)
• Survey on Vision-Language-Action Models [0.2636873872510828]
  This work does not represent original research, but highlights how AI can help automate literature reviews.<n>Future research will focus on developing a structured framework for AI-assisted literature reviews.
  arXiv  Detail & Related papers  (2025-02-07T11:56:46Z)
• Generative Physical AI in Vision: A Survey [78.07014292304373]
  Gene Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication.<n>This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content.<n>As generative models evolve to increasingly integrate physical realism and dynamic simulation, their potential to function as "world simulators" expands.
  arXiv  Detail & Related papers  (2025-01-19T03:19:47Z)
• Generative Artificial Intelligence: A Systematic Review and Applications [7.729155237285151]
  This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI. The major impact that generative AI has made to date, has been in language generation with the development of large language models. The paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.
  arXiv  Detail & Related papers  (2024-05-17T18:03:59Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• From Textual Experiments to Experimental Texts: Expressive Repetition in "Artificial Intelligence Literature" [0.0]
  AI literature integrates primitive problems including machine thinking, text generation, and machine creativity. In the early stage, the mutual support between technological path and artistic ideas turned out to be a failure.
  arXiv  Detail & Related papers  (2022-01-07T02:44:58Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

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.