IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-04633-1.html
   My bibliography  Save this article

Interpreting text corpora from androids-related stories using large language models: “Machines like me” by Ian McEwan in generative AI

Author

Listed:
  • Simona-Vasilica Oprea

    (Bucharest University of Economic Studies)

  • Adela Bâra

    (Bucharest University of Economic Studies)

Abstract

Ian McEwan’s “Machines like me” presents a scenario where androids demonstrate a limited understanding of the world. In this study, we feed the content of “Machines like me” novel into a chatbot powered by LLMs to analyze how it interprets various questions posed as prompts. We create a private chatbot based on OpenAI API using in-context learning and LangChain orchestration for evaluation. Our focus is on assessing the chatbot’s accuracy in responding, its level of world understanding, and the consistency of its answers. Our findings reveal that chatbots, similar to the android in the novel, still exhibit a lack of world understanding. This limitation, coupled with their tendency to produce hallucinatory responses, can hinder their ability to correctly interpret and respond to textual input. Further, we compare the private chatbot with ChatGPT-4 and evaluate the results.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra, 2025. "Interpreting text corpora from androids-related stories using large language models: “Machines like me” by Ian McEwan in generative AI," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04633-1
    DOI: 10.1057/s41599-025-04633-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-04633-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-025-04633-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Altman, Micah & McDonald, Michael P., 2011. "BARD: Better Automated Redistricting," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i04).
    2. Iyad Katib & Fatmah Y. Assiri & Hesham A. Abdushkour & Diaa Hamed & Mahmoud Ragab, 2023. "Differentiating Chat Generative Pretrained Transformer from Humans: Detecting ChatGPT-Generated Text and Human Text Using Machine Learning," Mathematics, MDPI, vol. 11(15), pages 1-19, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barry Burden & Corwin Smidt, 2020. "Evaluating Legislative Districts Using Measures of Partisan Bias and Simulations," SAGE Open, , vol. 10(4), pages 21582440209, December.
    2. Gopalan, Ram & Hachadoorian, Lee & Kimbrough, Steven O. & Murphy, Frederic H., 2024. "Selecting good redistricting plans from a large pool of available plans using the efficient frontier," Omega, Elsevier, vol. 124(C).
    3. Bivand, Roger, 2011. "Geocomputation and open source software: components and software stacks," Discussion Paper Series in Economics 23/2011, Norwegian School of Economics, Department of Economics.
    4. Kyle Gatesman & James Unwin, 2018. "Lattice Studies of Gerrymandering Strategies," Papers 1808.02826, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04633-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.