AI-Generated Text Detection Using RoBERTa: A Generalizability and Explainability Analysis

With the rise of AI-generated text, the need for efficient detectors that perform well on various kinds of text generated by different models and prompts is increasing. We trained and evaluated three detectors: fine-tuned RoBERTa, trained an RoBERTa based adapter and applied adapter fusion on an AI-generated text classification task. The detectors are tested for generalisation on three unseen datasets containing various generation models, generation types, and text styles. All three detectors performed well on the three test sets, outperforming the baselines introduced with the test sets. We found that completely AI-generated text is easier to detect than text that has been manipulated, paraphrased, or rewritten. In contrast, text of less than 50 words was harder to detect than longer text. While texts generated through translating, paraphrasing, and rewriting were better recognized by adapter fusion in most settings; fine-tuned RoBERTa yielded the best overall results. Using transformers-interpret as explainability method and POS-tagging, conjunctions (CC) were identified as characteristics of AI-generated text, whereby personal pronouns (PRP), verbs (present tense; VBP) and modals (MD) were identified as indicators for human-written text, independent of generation models and detectors viewed.