# Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection

arXiv: https://arxiv.org/abs/2604.04932 · HTML: https://mlsanity.com/paper/2604.04932/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 51 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 30 of 33)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [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)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [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)
- [Do LLMs write like humans? Variation in grammatical and rhetorical styles](https://mlsanity.com/paper/2410.16107/) (2024)
- [DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning](https://mlsanity.com/paper/2410.20964/) (2024)
- [Raidar: geneRative AI Detection viA Rewriting](https://mlsanity.com/paper/2401.12970/) (2024)
- [LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?](https://mlsanity.com/paper/2401.05952/) (2024)
- [Origin Tracing and Detecting of LLMs](https://mlsanity.com/paper/2304.14072/) (2023)
- [Few-Shot Detection of Machine-Generated Text using Style Representations](https://mlsanity.com/paper/2401.06712/) (2024)
- [DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing](https://mlsanity.com/paper/2110.04518/) (2021)
- [Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack](https://mlsanity.com/paper/2404.01907/) (2024)
- [Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore](https://mlsanity.com/paper/2405.04286/) (2024)
- [Beemo: Benchmark of Expert-edited Machine-generated Outputs](https://mlsanity.com/paper/2411.04032/) (2024)
- [Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling](https://mlsanity.com/paper/2402.09199/) (2024)
- [DAMAGE: Detecting Adversarially Modified AI Generated Text](https://mlsanity.com/paper/2501.03437/) (2025)
- [Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing](https://mlsanity.com/paper/2502.15666/) (2025)
- [LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation](https://mlsanity.com/paper/2504.20013/) (2025)
- [Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs](https://mlsanity.com/paper/2402.10586/) (2024)
- [Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education](https://mlsanity.com/paper/2307.12267/) (2023)
- [Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text](https://mlsanity.com/paper/2307.11380/) (2023)
- [DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning](https://mlsanity.com/paper/2510.17489/) (2025)

## Built on by (most cited first, 3 of 3)

- [D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection](https://mlsanity.com/paper/2608.27380/) (2026)
- [MixDetect: Word-Level Localization and Quantification of AI Editing](https://mlsanity.com/paper/2609.32625/) (2026)
- [LA-CPD: Local-Evidence-Aware Change-Point Detection for Human-LLM Authorship Segmentation](https://mlsanity.com/paper/2609.33787/) (2026)

## Abstract (arXiv)

The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only distinguish pure human/LLM text or collaborative text at best. This remains insufficient for the nuanced regulation, as the LLM-polished human text and humanized LLM text often trigger different policy consequences. In this paper, we explore fine-grained LLM-generated text detection under a rigorous four-class setting. To handle such complexities, we propose RACE (Rhetorical Analysis for Creator-Editor Modeling), a fine-grained detection method that characterizes the distinct signatures of creator and editor. Specifically, RACE utilizes Rhetorical Structure Theory (RST) to construct a logic graph for the creator's foundation while extracting Elementary Discourse Unit (EDU)-level features for the editor's style. Experiments show that RACE outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation.