@inproceedings{bayani-2024-testing,
    title = "Testing the Depth of {C}hat{GPT}`s Comprehension via Cross-Modal Tasks Based on {ASCII}-Art: {GPT}3.5`s Abilities in Regard to Recognizing and Generating {ASCII}-Art Are Not Totally Lacking",
    author = "Bayani, David",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-eacl.139/",
    pages = "2063--2077",
    abstract = "In the months since its release, ChatGPT and its underlying model, GPT3.5, have garnered massive attention, due to their potent mix of capability and accessibility. While a niche industry of papers have emerged examining the scope of capabilities these models possess, language {---} whether natural or stylized like code {---} has been the vehicle to exchange information with the network. Drawing inspiration from the multi-modal knowledge we`d expect an agent with true understanding to possess, we examine GPT3.5`s aptitude for visual tasks, where the inputs feature ASCII-art without overt distillation into a lingual summary. In particular, we scrutinize its performance on carefully designed image recognition and generation tasks. An extended version of this write-up is available at: https://arxiv.org/abs/2307.16806 ."
}