Zarcone, Alessandra (Prof. Dr.-phil.)
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EduQuick: A Dataset Toward Evaluating Summarization of Informal Educational Content for Social Media
(2023)
This study explores the capacity of large language models (LLMs) to efficiently generate summaries of informal educational content tailored for platforms like TikTok. It also investigates how both humans and LLMs assess the quality of these summaries, based on a series of experiments, exploring the potential replacement of human evaluation with LLMs. Furthermore, the study delves into how experienced content creators perceive the utility of automatic summaries for TikTok videos. We employ strategic prompt selection techniques to guide LLMs in producing engaging summaries based on the characteristics of viral TikTok content, including hashtags, captivating hooks, storytelling, and user engagement. The study leverages OpenAI’s GPT-4 model to generate TikTok content summaries, aiming to align them with the essential features identified. By employing this model and incorporating human evaluation and expert assessment, this research endeavors to shed light on the intricate dynamics of modern content creation, where AI and human ingenuity converge. Ultimately, it seeks to enhance strategies for disseminating and evaluating educational information effectively in the realm of social media.
Bubble up – A Fine-tuning Approach for Style Transfer to Community-specific Subreddit Language
(2023)
Different online communities (social media bubbles) can be identified with their use of language. We looked at different social media bubbles and explored the task of translating between the language of one bubble into another while maintaining the intended meaning. We collected a dataset of Reddit comments from 20 different Subreddits and for a smaller subset of them we obtained style-neutral versions generated by a large language model. Then we used the dataset to fine-tune different (smaller) language models to learn style transfers between social media bubbles. We evaluated the models on unseen data from four unseen social media bubbles to assess to what extent they had learned the style transfer task and compared their performance with the zero-shot performance of a larger, non-fine tuned, language model. We show that with a small amount of fine-tuning the smaller models achieve satisfactory performance, making them more attractive than a larger, more resource-intensive model.