Few-Shot Detection of Machine-Generated Text using Style Representations (2401.06712v3)

Abstract: The advent of instruction-tuned LLMs that convincingly mimic human writing poses a significant risk of abuse. However, such abuse may be counteracted with the ability to detect whether a piece of text was composed by a LLM rather than a human author. Some previous approaches to this problem have relied on supervised methods by training on corpora of confirmed human- and machine- written documents. Unfortunately, model under-specification poses an unavoidable challenge for neural network-based detectors, making them brittle in the face of data shifts, such as the release of newer LLMs producing still more fluent text than the models used to train the detectors. Other approaches require access to the models that may have generated a document in question, which is often impractical. In light of these challenges, we pursue a fundamentally different approach not relying on samples from LLMs of concern at training time. Instead, we propose to leverage representations of writing style estimated from human-authored text. Indeed, we find that features effective at distinguishing among human authors are also effective at distinguishing human from machine authors, including state-of-the-art LLMs like Llama-2, ChatGPT, and GPT-4. Furthermore, given a handful of examples composed by each of several specific LLMs of interest, our approach affords the ability to predict which model generated a given document. The code and data to reproduce our experiments are available at https://github.com/LLNL/LUAR/tree/main/fewshot_iclr2024.

• The paper introduces a novel style-based detection method that leverages few-shot learning to identify machine-generated text.
• It demonstrates high accuracy across advanced language models like GPT-4, Llama 2, and ChatGPT with minimal false positives.
• The findings highlight the adaptability of style representations in countering adversarial adaptations and ensuring robust real-world detection.

Introduction

In the field of AI, LLMs have become increasingly sophisticated, able to generate text nearly indistinguishable from human writing. While these advancements have many positive applications, they also pose a risk when used maliciously for plagiarism, disinformation, and other deceptive practices. The challenge is detecting whether text has been generated by a machine, particularly as models evolve and new ones are introduced, often surpassing the capabilities of existing detection systems. Traditional detection methods depend heavily on supervised learning with large datasets of machine vs. human text but are often unsuitable for next-generation models not present in the training data.

Style-based Detection Approach

A novel approach is proposed that shifts the focus from content to style. Unlike content that can vary according to topics or prompts, an author's writing style carries idiosyncratic features across their work. This method capitalizes on learned style representations from vast human-authored texts to distinguish between human and machine writing. Initial findings reveal that attributes which pinpoint different human authors can also be leveraged to discern human authorship from machine-generated content, even from advanced LLMs like Llama 2, ChatGPT, and GPT-4. An advantage of this technique is its adaptability—it can be effective with minimal examples from LLMs, hence termed "few-shot detection."

Methodology and Experimentation

The research details several experiments and methodologies. A new yardstick is defining effectiveness by the ability to detect machine-produced content with minimal false-alarms—critical for practical scenarios such as academic plagiarism detection or filtering out AI-generated spam. The paper contrasts its approach with well-known methods like OpenAI's text classifier, highlighting the limitations when facing novel, unseen machine-written content.

For several style representation techniques, the paper shows that they are potent in identifying machine text, even when trained mostly on human writing. These techniques include adapting multi-domain data (incorporating stylistic elements from different platform sources) and training on documents generated by accessible LLMs to improve text detection from more powerful or emerging models. The research also involves creating openly accessible datasets for the scholarly community, promoting further exploration and validation of detection methods.

Evaluating Robustness

Another essential component of the method is its robustness to countermeasures like text paraphrasing designed to thwart detection. Here, they demonstrate how the approach remains effective even against adversarially adapted content. Continuously evolving models necessitate a framework that can handle the ever-changing landscape together with the need to craft strategies that can immediately identify abuse by unknown LLMs.

Conclusion and Impact

The proposed method is innovative in using style as a detection signal, delivering a practical, scalable, and adaptable tool to combat machine-text abuse while maintaining lower false positives. The research emphasizes that as LLMs become more mainstream, strategies to distinguish AI-authorship from human writing will be vital. Recognizing the broader impact, the future work will include extending approaches to languages beyond English, most critical for global languages with rich internet presences.

As AI continues to advance, transparency, accountability, and controls for LLMs are essential, and researchers are committed to contributing tools that empower stakeholders across varied sectors to uphold integrity in information dissemination. The results encourage prompt adoption of this methodology in settings that require an immediate detection line of defense.