Generative AI-Authored Immigration Narratives: Sentiment Analysis and Fictionality
16.05.2024, 18-20 Uhr c.t., Senatssaal (K.11.07) & Zoom
Generative AI-Authored Immigration Narratives: Sentiment Analysis and Fictionality
Gastvortrag von Prof. Dr. Torsa Ghosal (California State University, Sacramento)
Researchers in the fields of machine learning and artificial intelligence have sought to leverage digital technologies to model story structures for several decades now (see Meehan 1977; Dehn 1981; Gervás 2016). The current generation of large language models (LLMs) such as GPT-4 and PaLM can be prompted to produce sufficiently complex narratives. As a result, technology companies are pushing forward a sweeping “cultural narrative” (Nadel 1995) wherein AI is the ‘future of storytelling.’ In contrast, cultural narratives circulating in creative sectors about LLMs, especially since the public release of OpenAI's ChatGPT, remain concerned with plagiarism, increased circulation of fake news, and the devaluing of creative labor. Nonetheless, artists are experimenting with generative AI. Authors like Adi Robertson and Vauhini Vara report that narratives they co-wrote with AI are excessively sentimental. K. Allado-McDowell claims that AI helped her generate the “cringiest” autofiction. Such creative experiments offer cursory insight into features of the data and parameters training the current generation of LLMs.
This presentation approaches AI-generated fictions as indexing how broad swathes of the online population thinks and feels—an archive of cultural narratives—and instrumentalizes narrative affordances of LLMs, trained on online public discourse, to grasp the “affective economies” (Ahmed 2004), or emotional collectives, forming around critical issues. I discuss findings of my ongoing project that assembles immigration-centric fictions generated through human-AI interactions with the aim of uncovering the underpinnings behind the training of transformer models, including but not limited to studying the data corpus and embeddings from open source LLMs. My method involves creatively engineering prompts that generate relevant immigration narratives from the transformer-based models (GPT), close and distant reading the generated results, as well as theoretically framing and understanding the findings by combining narratology and scholarship on natural language processing.
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