SzegedAI at GenAI Detection Task 1: Beyond Binary - Soft-Voting Multi-Class Classification for Binary Machine-Generated Text Detection Across Diverse Language Models

Mihaly Kiss, Gábor Berend

Abstract: This paper describes the participation of the SzegedAI team in Subtask A of Task 1 at the COLING 2025 Workshop on Detecting AI-Generated Content. Our solutions investigate the effectiveness of combining multi-class approaches with ensemble methods for detecting machine-generated text. This approach groups models into multiple classes based on properties such as model size or generative capabilities. Additionally, we employ a length-based method, utilizing specialized expert models designed for specific text length ranges. During inference, we condense multi-class predictions into a binary outcome, categorizing any label other than human as AI-generated. The effectiveness of both standard and snapshot ensemble techniques is evaluated. Although not all multi-class configurations outperformed the binary setup, our findings indicate that the combination of multi-class training and ensemble methods can enhance performance over single-method or binary approaches.

External IDs:dblp:conf/coling/KissB25