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arxiv: 2604.04932 · v2 · submitted 2026-04-06 · 💻 cs.CL

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

Yang Li , Qiang Sheng , Zhengjia Wang , Yehan Yang , Danding Wang , Juan Cao This is my paper

Pith reviewed 2026-05-10 19:40 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM-generated text detectionfine-grained classificationRhetorical Structure Theorycreator-editor modelingdiscourse analysissynthetic texttext regulationNLP
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The pith

RACE separates creator intent from editor style to enable four-class detection of LLM-generated text.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to improve detection of synthetic text by moving past binary or ternary labels to a four-class scheme that distinguishes pure human text, humanized LLM text, LLM-polished human text, and pure LLM text. These distinctions matter for regulation because each type can trigger different policy responses. RACE builds a logic graph from Rhetorical Structure Theory to capture the creator's foundation and pulls Elementary Discourse Unit features to isolate the editor's style, allowing more precise identification than earlier methods.

Core claim

RACE utilizes Rhetorical Structure Theory to construct a logic graph for the creator's foundation while extracting Elementary Discourse Unit-level features for the editor's style, enabling fine-grained four-class classification of LLM-generated text that outperforms twelve baselines with low false alarms.

What carries the argument

RACE (Rhetorical Analysis for Creator-Editor Modeling), which builds RST logic graphs to represent creator intent and extracts EDU features to capture editor modifications.

If this is right

  • Enables regulators to apply different rules to LLM-polished human text versus humanized LLM text.
  • Reduces false alarms when identifying nuanced forms of LLM involvement.
  • Supports more accurate monitoring of collaborative human-LLM text production.
  • Provides a concrete method that scales beyond coarse binary or ternary detection settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dual-role separation may apply to other AI-assisted creative tasks where intent and refinement need disentangling.
  • Detection systems built this way could inform tiered policies that treat light AI editing differently from full AI generation.
  • Testing the same features on non-English texts or newer LLMs would show whether the rhetorical signatures remain stable.

Load-bearing premise

Rhetorical Structure Theory graphs and Elementary Discourse Unit features can reliably separate creator intent from editor style even when LLMs perform both roles or edits are subtle.

What would settle it

A dataset of texts where LLMs both create and subtly edit content, producing RST graphs and EDU features that overlap across the four classes and cause frequent misclassifications.

Figures

Figures reproduced from arXiv: 2604.04932 by Danding Wang, Juan Cao, Qiang Sheng, Yang Li, Yehan Yang, Zhengjia Wang.

Figure 1
Figure 1. Figure 1: Illustration of our research scope. (a) A Creator-Editor framework for categorizing differ￾ent types of texts in fine-grained LLM-generated text detection. (b) Comparison of the existing set￾tings and the complex 4-class setting that we focus on in this paper. While the surge of Large Language Models (LLMs) (Ope￾nAI, 2025b, Yang et al., 2025a) has revolutionized content creation and inspired a diverse rang… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of RST relations. (a) Divergence of Creators: Human creators build deeper rhetorical hierarchies (e.g., Attribution, Background), whereas LLMs produce flatter struc￾tures relying on surface-level relations (e.g., Elabo￾ration, Evaluation). (b) LLM-Polished: underlying human architecture persists. (c) Humanized: un￾derlying LLM architecture persists. Rhetorical Structure Theory (RST) is a descr… view at source ↗
Figure 3
Figure 3. Figure 3: Overall architecture of RACE. Given a text piece, RACE (a) first captures both creator and editor traces through rhetorical structure construction and elementary discourse unit extraction. (b) These dual traces are then transformed into a logic-aware graph, where both linguistic expression and logical organization signals are encoded into node features via descendant span pooling and relation-aware project… view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of detection performance of CoCo and our proposed RACE across varying text lengths. Impact of Text Length Variations. We investigate how input text length affects detection performance [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

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 to construct a logic graph for the creator's foundation while extracting Elementary Discourse Unit-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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript introduces RACE for fine-grained four-class LLM-generated text detection (pure human, pure LLM, LLM-polished human, humanized LLM). It constructs an RST-based logic graph to capture the creator's foundational structure and extracts EDU-level features to isolate the editor's modifications, claiming superior performance over 12 baselines with low false alarms for policy-relevant regulation.

Significance. If the role-specific separation holds under the targeted conditions, the approach would advance beyond binary/ternary detectors by providing interpretable, discourse-theoretic features aligned with regulatory distinctions between human and synthetic contributions.

major comments (1)
  1. [Experiments] Experiments section: no ablation is reported for the regime in which creator and editor roles are performed by the same LLM (or minimal edits). This test is load-bearing for the claim that RST graphs encode creator intent while EDU features isolate editor style, as the final discourse tree would otherwise reflect a single generation process rather than dual roles; without it, gains over baselines may stem from easier synthetic markers instead of the intended fine-grained modeling.
minor comments (3)
  1. [Abstract] Abstract: the claim of outperforming 12 baselines would be strengthened by including at least one key metric (e.g., macro-F1) and dataset size to allow immediate evaluation of the reported gains.
  2. [Method] Method: a worked example or figure showing an RST logic graph and corresponding EDU features on a short text snippet would clarify how the two components are extracted and combined.
  3. [Results] Results: ensure all tables report error bars or statistical significance tests alongside the 12-baseline comparisons to support the low false-alarm assertion.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address the single major comment point by point below.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: no ablation is reported for the regime in which creator and editor roles are performed by the same LLM (or minimal edits). This test is load-bearing for the claim that RST graphs encode creator intent while EDU features isolate editor style, as the final discourse tree would otherwise reflect a single generation process rather than dual roles; without it, gains over baselines may stem from easier synthetic markers instead of the intended fine-grained modeling.

    Authors: We agree that an explicit ablation isolating the case of identical LLM instances for both creator and editor roles (including minimal-edit regimes) would provide stronger evidence that the RST logic graph and EDU features capture distinct role signatures rather than generic synthetic artifacts. Our four-class datasets already incorporate humanized LLM and LLM-polished human texts generated via separate model calls, but we did not report a controlled same-LLM ablation. In the revised manuscript we will add this experiment, using the same base LLM for creation followed by controlled self-editing at varying intensities, and compare against the dual-role setting to quantify the contribution of the role-specific features. revision: yes

Circularity Check

0 steps flagged

No circularity: method uses standard RST and EDU extraction without self-referential fits or load-bearing self-citations

full rationale

The paper's core proposal in the abstract defines RACE via Rhetorical Structure Theory for creator logic graphs and Elementary Discourse Unit features for editor style, with performance claims resting on experiments against baselines. No equations, parameter-fitting steps, or self-citations are described that would reduce any prediction or uniqueness claim to the inputs by construction. The derivation chain is self-contained against external discourse theory and standard feature extraction, with no evidence of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach relies on Rhetorical Structure Theory as a pre-existing discourse framework and on Elementary Discourse Units as standard segmentation units; no new constants, free parameters, or postulated entities are introduced in the abstract.

axioms (2)
  • domain assumption Rhetorical Structure Theory can be applied to construct a logic graph that captures the creator's foundational structure
    Invoked directly in the description of the creator modeling stage.
  • domain assumption Elementary Discourse Unit-level features can isolate the editor's stylistic contributions
    Invoked directly in the description of the editor modeling stage.

pith-pipeline@v0.9.0 · 5473 in / 1402 out tokens · 58057 ms · 2026-05-10T19:40:57.031848+00:00 · methodology

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Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

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    How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection

    How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection.Preprint, arXiv:2301.07597. Xun Guo, Shan Zhang, Yongxin He, Ting Zhang, Wanquan Feng, Haibin Huang, and Chongyang Ma. 2024. DeTeCtive: detecting AI-generated text via multi-level contrastive learning. InProceedings of the 38th International Conference on Neural Informatio...

    work page arXiv 2024

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    InThe Twelfth International Conference on Learning Representations

    Few-Shot Detection of Machine-Generated Text using Style Representations. InThe Twelfth International Conference on Learning Representations. Jinyan Su, Terry Zhuo, Di Wang, and Preslav Nakov. 2023. DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. InFindings of the Association for Computational Linguistics: EMN...

    work page arXiv 2023

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    arms race

    Real, Fake, or Manipulated? Detecting Machine-Influenced Text. InFindings of the Association for Computational Linguistics: EMNLP 2025, pages 15022–15037. Association for Computational Linguistics. YuxiaWang, JonibekMansurov, PetarIvanov, JinyanSu, ArtemShelmanov, AkimTsvigun, ChenxiWhitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arno...

    work page arXiv 2025