Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. With the introduction of GPT-3 \citep{brown2020language}, made accessible to the public through ChatGPT, the gates were open to the generation of high-quality text through AI. This leads to a sort of arms race both in integrating AI into customer-facing products, as well as in developing the models themselves, leading, among others, to Facebook's Llama \citep{touvron2023llama} and the open source model Mistral \citep{jiang2023mistral}.

Many fields have seen a dramatic increase in the use of LLMs, including arts, education, software development, and many more. Initial public reaction to the popularisation of LLM-powered chat interfaces already highlighted its potential problems for plagiarism detection in scientific, educational and other contexts \citep{Gao2021}. In a landmark development, the 5-month-long strike of the Writer's Guild of America\footnote{\url{https://www.vox.com/culture/2023/9/24/23888673/wga-strike-end-sag-aftra-contract}} resulted in an agreement which included safeguards for writers against the use of artificial intelligence.\footnote{\url{https://www.nbclosangeles.com/news/local/hollywood-writers-safeguards-against-ai-wga-agreement/3233064/}} 

The widespread use of language generation technologies is only expected to grow. However, this development is accompanied by a surging need to automate the process of flagging machine-generated text.

\citealp{10177704} offers a comprehensive survey of machine-generated text detection strategies. Statistical methods, with global text vector representations, were among the early strategies adopted to tackle the issue. These feature vectors include, for example, TF-IDF, frequency features investigating word or n-gram distributions, readability-related features such as the Gunning-Fog index, or linguistic features such as POS-tag distributions and coreference resolution relationships within a text. The subsequently introduced neural approaches show better performance, even when paired with the aforementioned text representation strategies. Most prominently, transformer fine-tuning has established itself as more or less of a standard, with RoBERTa \citep{liu2019roberta} in particular being the most strongly represented model. Zero-shot approaches, optionally coupled with fine-tuning, have also seen experimentation, but have been observed to generalise poorly across domains. 

Task 8 at SemEval-2024 \citep{semeval2024task8} is a shared task built around the idea of detecting machine-generated texts across a variety of domains and setups. It is structured across three subtasks (subtask A, subtask B, subtask C), our team decided to only tackle subtasks A and C. Subtask A is a binary classification task, with the objective of determining whether a text is human- or machine-generated. The subtask has a monolingual (English) and a multilingual track - our team only submitted for the monolingual track. Subtask C is a boundary detection task: here, texts have a human segment followed by a machine-generated segment. The objective of the subtask is to correctly predict the boundary index.

Our approach for both tasks was to try to obtain competitive solutions with low resource cost. For this reason we deployed two different model classes: LSTM-based models \citep{hochreiter1997long} and Multilayer Perceptrons (MLP) \citep{popescu2009} and relied on  various strategies of representing the input texts, either at the token or at the text level. We experimented with character-level approaches, systems relying on pretrained Word2vec \citep{mikolov2013efficient} embeddings, and linguistically motivated features at the text level through spaCy \citep{honnibal2020spacy} and the TextDescriptives \citep{Hansen_2023} package.