Learn-to-Distance: Distance Learning for Detecting LLM-Generated Text

TL;DR

This paper introduces a novel adaptive distance learning algorithm for detecting LLM-generated text, demonstrating superior performance over existing methods across various models and settings.

Contribution

It presents a geometric analysis of rewrite-based detection and develops an adaptive distance learning approach that improves detection accuracy.

Findings

Achieves up to 75.4% relative improvement over baselines

Demonstrates generalization across multiple LLMs like GPT, Claude, and Gemini

Provides a publicly available Python implementation

Abstract

Modern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human-like text raises serious concerns about misinformation and academic integrity, making it an urgent need for reliable algorithms to detect LLM-generated content. In this paper, we start by presenting a geometric approach to demystify rewrite-based detection algorithms, revealing their underlying rationale and demonstrating their generalization ability. Building on this insight, we introduce a novel rewrite-based detection algorithm that adaptively learns the distance between the original and rewritten text. Theoretically, we demonstrate that employing an adaptively learned distance function is more effective for detection than using a fixed distance. Empirically, we conduct extensive experiments with over 100 settings, and find that our approach demonstrates superior performance over baseline algorithms in the majority of scenarios. In particular, it achieves relative improvements from 54.3% to 75.4% over the strongest baseline across different target LLMs (e.g., GPT, Claude, and Gemini). A python implementation of our proposal is publicly available at https://github.com/Mamba413/L2D.