In recent years, there has been an increase in the issue of bullying, particularly in academic settings. This degree project examines the use of supervised machine learning techniques to identify bullying in text data from school surveys provided by the Friends Foundation. It evaluates various traditional algorithms such as Logistic Regression, Naive Bayes, SVM, Convolutional neural networks (CNN), alongside a Retrieval-Augmented Generation (RAG) model using Llama 3, with a primary goal of achieving high recall on the texts consisting of bullying while also considering precision, which is reflected in the use of the F3-score. The SVM model emerged as the most effective among the traditional methods, achieving the highest F3-score of 0.83. Although the RAG model showed promising recall, it suffered from very low precision, resulting in a slightly lower F3-score of 0.79. The study also addresses challenges such as the small and imbalanced dataset as well as emphasizes the importance of retaining stop words to maintain context in the text data. The findings highlight the potential of advanced machine learning models to significantly assist in bullying detection with adequate resources and further refinement.

This study introduces an automated evaluation method for large language model (LLM) based chatbots in educational settings, utilizing LLM-as-a-Judge to assess their performance. Our results demonstrate the efficacy of this approach in evaluating the accuracy of three LLM-based chatbots (Llama 3 70B, ChatGPT 4, Gemini Advanced) across two subjects: history and biology. The analysis reveals promising performance across different subjects. On a scale from 1 to 5 describing the correctness of the judge itself, the LLM judge’s average scores for correctness when evaluating each chatbot on history related questions are 3.92 (Llama 3 70B), 4.20 (ChatGPT 4), 4.51 (Gemini Advanced); for biology related questions, the average scores are 4.04 (Llama 3 70B), 4.28 (ChatGPT 4), 4.09 (Gemini Advanced). This underscores the potential of leveraging the LLM-as-a-judge strategy to evaluate the correctness of responses from other LLMs.