Technological Advancements in Digital Collaboration with Generative AI and Large Language Models

Permanent URI for this collectionhttps://hdl.handle.net/10125/109852

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    The Visual Analogs of Linguistic Concepts and Their Implications on Generative AI
    (2025-01-07) Combs, Kara; Bihl, Trevor
    Many visual generative artificial intelligence (AI) models use textual “prompts” as input(s) to guide the development of the resulting image(s). Converting text to images utilizes pragmatics and semantics, which can make an impact on the output. To facilitate more precise prompting, we propose the three-dimensional vector space of textual similarity which uses textual representation, auditory representation, and meaning similarity as its axes. Next, we show that meaning similarity between two words does not necessarily yield visual similarity between corresponding AI-generated images of those words. We quantitively justify this by leveraging eight image generators to generate images for abstract and concrete synonyms, antonyms, and hypernyms-hyponym pairs and compare their image-image CLIPScores to their corresponding text-text CLIPScores. Across all models and relationship types the average similarity comparing text-text and image-image similarity decreased from 92.8% to 70.1% for synonyms, 89% to 58.9% for antonyms, and 85.6% to 68.1% for hypernym-hyponym pairs.
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    Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
    (2025-01-07) Liu, Haoxin; Zhang, Wenli; Xie, Jiaheng; Kim, Buomsoo; Zhang, Zhu; Chai, Yidong; Ram, Sudha
    The recent breakthrough in Artificial Intelligence (AI) has resulted in a profound impact on various domains including healthcare. Among them, this study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. We propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. On the depression detection task, our method (F1 = 0.975~0.978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0.760) and architecture engineering (F1 = 0.756). Our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978). In addition to the technical contributions, our proposed framework has the potential to improve the well-being of patients, control costs, and establish a more efficient and accessible healthcare system.
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    Is the Larger the Better? An Exploratory Study into Human-Large Language Model Collaboration
    (2025-01-07) Tao, Jie; Fang, Xing; Zhou, Lina
    Large language models (LLMs) have garnered considerable attention in both academics and industry. Given an array of LLMs available, one of the primary challenges lies in their selection and adaptation strategies. Although LLMs are generally large, they still vary significantly in size and larger LLMs consume significantly more computing resources. This prompts the inquiry into whether larger models perform better. In addition, there is a widespread recognition of the power of LLMs in performing open-ended or generative tasks. However, how to use LLMs to address a closed-ended problem remains under explored. The exploration of human-LLM collaboration on close-ended problem has been even more sparse. This research aims to address the above limitations by comparing different types of state-of-the-art adaptation strategies for LLMs, including in-context learning and fine-tuning. Moreover, it employs multi-class multi-label classification - a close-ended problem to empirically evaluate those adaptation strategies. The research findings provide valuable insights and recommendations for human users considering deploying LLMs for close-ended problems.