Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking

TL;DR

This paper presents Advacheck's multi-task Transformer-based system for detecting AI-generated text, leveraging domain-aware classification to improve accuracy and achieve top ranking in the GenAI Detection Task 1 competition.

Contribution

The paper introduces a novel multi-task architecture with domain-aware classification heads that enhances AI-generated text detection performance.

Findings

Multi-task learning outperforms single-task models.

Domain-aware classification improves detection accuracy.

Embeddings form a cluster structure revealing task relationships.

Abstract

The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07% macro F1-score on the test set and bypass the baseline by 10%. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space.