# Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood

arXiv: https://arxiv.org/abs/2406.19874 · HTML: https://mlsanity.com/paper/2406.19874/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://mlsanity.com/paper/2102.01454/) (2021)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text](https://mlsanity.com/paper/2305.17359/) (2023)
- [If beam search is the answer, what was the question?](https://mlsanity.com/paper/2010.02650/) (2020)
- [Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings](https://mlsanity.com/paper/1908.03477/) (2019)
- [Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech](https://mlsanity.com/paper/1805.12589/) (2018)
- [GPT-who: An Information Density-based Machine-Generated Text Detector](https://mlsanity.com/paper/2310.06202/) (2023)
- [FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy](https://mlsanity.com/paper/2305.10307/) (2023)
- [Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling](https://mlsanity.com/paper/2105.03641/) (2021)

## Built on by (most cited first, 24 of 24)

- [Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection](https://mlsanity.com/paper/2412.11506/) (2024)
- [The Harmonic Structure of Information Contours](https://mlsanity.com/paper/2506.03902/) (2025)
- [Large Language Models Penetration in Scholarly Writing and Peer Review](https://mlsanity.com/paper/2502.11193/) (2025)
- [Decoupling Content and Expression: Two-Dimensional Detection of AI-Generated Text](https://mlsanity.com/paper/2503.00258/) (2025)
- [Identifying the Periodicity of Information in Natural Language](https://mlsanity.com/paper/2510.27241/) (2025)
- [When AI Settles Down: Late-Stage Stability as a Signature of AI-Generated Text Detection](https://mlsanity.com/paper/2601.04833/) (2026)
- [Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model](https://mlsanity.com/paper/2604.21223/) (2026)
- [TempTest: Local Normalization Distortion and the Detection of Machine-generated Text](https://mlsanity.com/paper/2503.20421/) (2025)
- [A Theoretical Analysis of Detecting Large Model-Generated Time Series](https://mlsanity.com/paper/2511.07104/) (2025)
- [Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection](https://mlsanity.com/paper/2602.01240/) (2026)
- [Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective](https://mlsanity.com/paper/2511.00988/) (2025)
- [Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection](https://mlsanity.com/paper/2602.08031/) (2026)
- [WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform](https://mlsanity.com/paper/2606.23336/) (2026)
- [A Free Probabilistic Framework for Analyzing the Transformer-based Language Models](https://mlsanity.com/paper/2506.16550/) (2025)
- [AI-Generated Text is Non-Stationary: Detection via Temporal Tomography](https://mlsanity.com/paper/2508.01754/) (2025)
- [Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence](https://mlsanity.com/paper/2510.07500/) (2025)
- [SearchLLM: Detecting LLM Paraphrased Text by Measuring the Similarity with Regeneration of the Candidate Source via Search Engine](https://mlsanity.com/paper/2601.16512/) (2026)
- [Surprisal reveals diversity gaps in image captioning and different scorers change the story](https://mlsanity.com/paper/2511.04754/) (2025)
- [Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection](https://mlsanity.com/paper/2605.16107/) (2026)
- [Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement](https://mlsanity.com/paper/2605.23190/) (2026)
- [The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales](https://mlsanity.com/paper/2606.11371/) (2026)
- [Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks](https://mlsanity.com/paper/2606.31074/) (2026)
- [Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?](https://mlsanity.com/paper/2608.30948/) (2026)
- [IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection](https://mlsanity.com/paper/2609.33585/) (2026)

## Abstract (arXiv)

Human and model-generated texts can be distinguished by examining the magnitude of likelihood in language. However, it is becoming increasingly difficult as language model's capabilities of generating human-like texts keep evolving. This study provides a new perspective by using the relative likelihood values instead of absolute ones, and extracting useful features from the spectrum-view of likelihood for the human-model text detection task. We propose a detection procedure with two classification methods, supervised and heuristic-based, respectively, which results in competitive performances with previous zero-shot detection methods and a new state-of-the-art on short-text detection. Our method can also reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. Our code is available at https://github.com/CLCS-SUSTech/FourierGPT