DeSegMa-IT’s shared tasks aim to test the robustness of machine-generated text (MGT) detectors by evaluating their performance under settings where the IID assumption does not hold. While state-of-the-art MGT detectors report high accuracy, such results often rely on unrealistic experimental settings: for example, relying on prior knowledge of the text generator, or failing to consider domain shifts and efficient fine-tuning - or post-tuning - strategies.
  In DeSegMa-IT, participants are challenged with two sub-tasks: \textit{(i)} document-level detection of MGTs and the \textit{(ii)} human-machine text segmentation.
  This paper describes the released dataset, discusses the systems submitted by participants, and provides an initial analysis of the obtained results.
