GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization

TL;DR

GigaCheck introduces a dual-strategy framework combining document-level authorship detection with span-level localization by treating generated text segments as objects, leveraging vision models for improved detection robustness.

Contribution

The paper presents a novel approach that adapts visual object detection models for precise localization of AI-generated text spans, enhancing detection robustness and generalization.

Findings

High accuracy in authorship classification across multiple benchmarks.

Effective localization of generated text spans using DETR-like models.

Demonstrated robustness and generalization of the approach across tasks.

Abstract

With the increasing quality and spread of LLM assistants, the amount of generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and the quality of generation continues to increase. At the same time, detection methods are advancing more slowly than generation models, making it challenging to prevent misuse of generative AI technologies. We propose GigaCheck, a dual-strategy framework for AI-generated text detection. At the document level, we leverage the representation learning of fine-tuned LLMs to discern authorship with high data efficiency. At the span level, we introduce a novel structural adaptation that treats generated text segments as "objects." By integrating a DETR-like vision model with linguistic encoders, we achieve precise localization of AI intervals, effectively transferring the robustness of visual object detection to the textual domain. Experimental results across three classification and three localization benchmarks confirm the robustness of our approach. The shared fine-tuned backbone delivers strong accuracy in both scenarios, highlighting the generalization power of the learned embeddings. Moreover, we successfully demonstrate that visual detection architectures like DETR are not limited to pixel space, effectively generalizing to the localization of generated text spans. To ensure reproducibility and foster further research, we publicly release our source code.