[Paper Review] G3Detector: General GPT-Generated Text Detector

Haolan Zhan, Xuanli He+3 more|arXiv (Cornell University)|May 22, 2023

Topic Modeling4 citations

TL;DR

G3Detector is a robust, general-purpose detector for identifying text generated by modern Large Language Models (LLMs), including GPT-4 and ChatGPT. It leverages a lightweight, architecture-agnostic approach that maintains high accuracy across diverse model architectures, decoding strategies, and even evasion techniques, significantly improving detection of state-of-the-art synthetic text.

ABSTRACT

The burgeoning progress in the field of Large Language Models (LLMs) heralds significant benefits due to their unparalleled capacities. However, it is critical to acknowledge the potential misuse of these models, which could give rise to a spectrum of social and ethical dilemmas. Despite numerous preceding efforts centered around distinguishing synthetic text, most existing detection systems fail to identify data synthesized by the latest LLMs, such as ChatGPT and GPT-4. In response to this challenge, we introduce an unpretentious yet potent detection approach proficient in identifying synthetic text across a wide array of fields. Moreover, our detector demonstrates outstanding performance uniformly across various model architectures and decoding strategies. It also possesses the capability to identify text generated utilizing a potent detection-evasion technique. Our comprehensive research underlines our commitment to boosting the robustness and efficiency of machine-generated text detection mechanisms, particularly in the context of swiftly progressing and increasingly adaptive AI technologies.

Motivation & Objective

• To address the growing challenge of detecting high-quality, synthetic text generated by state-of-the-art LLMs such as GPT-4 and ChatGPT.
• To overcome the limitations of existing detectors that fail to generalize across new LLM architectures and decoding strategies.
• To develop a detection system resilient against advanced evasion techniques used to bypass existing detectors.
• To ensure consistent and high performance across diverse domains and model configurations.
• To enhance the robustness and efficiency of machine-generated text detection in the face of rapidly evolving LLM technologies.

Proposed method

• The detector employs a lightweight, architecture-agnostic classification framework trained to distinguish between human and LLM-generated text.
• It leverages intrinsic statistical and linguistic patterns in text that remain consistent across diverse LLMs and generation strategies.
• The method is designed to be agnostic to model architecture, enabling broad generalization across different LLMs.
• It incorporates features sensitive to subtle artifacts introduced by advanced decoding strategies, including temperature sampling and nucleus sampling.
• The system is trained on a diverse, multi-domain dataset to ensure robustness across various text types and generation styles.
• It maintains high performance even when confronted with text generated using detection-evasion techniques.

Experimental results

Research questions

• RQ1 Can a single detection model generalize effectively across multiple LLM architectures and decoding strategies?
• RQ2 How does the detector perform on text generated by the latest models such as GPT-4 and ChatGPT?
• RQ3 To what extent can the detector identify text produced using advanced evasion techniques designed to bypass existing detectors?
• RQ4 Does the detector maintain high accuracy across diverse text domains and styles?
• RQ5 How does the model's performance compare to existing state-of-the-art detection methods?

Key findings

• G3Detector achieves high detection accuracy across a wide range of LLMs, including GPT-4 and ChatGPT, demonstrating strong generalization capabilities.
• The detector maintains consistent performance across various decoding strategies, such as temperature sampling and nucleus sampling.
• It successfully identifies text generated using advanced detection-evasion techniques, indicating resilience to adversarial generation methods.
• The model exhibits robust performance across diverse domains, confirming its generalization beyond specific text types.
• Its lightweight design enables efficient deployment without sacrificing detection accuracy.
• The detector outperforms existing methods in detecting synthetic text from state-of-the-art LLMs, particularly in cross-architecture and cross-strategy settings.