---
title: "Synergizing Stylometrics with Semantics: Dual-Path Framework"
description: "Building on this, they propose Stylometric-Semantic LLM Attribution (SSLA), which builds explicit stylistic profiles from three… 24 references."
authors: ["Xingyu Lu", "Yumeng Ma", "Xiang Zhou", "Shengli Gan", "Guiying Deng", "Yang Wen", "Yanbing Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1855/"
doi: "10.18653/v1/2026.findings-acl.1855"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lu-2026-synergizing-stylometrics-semantics-dual-path/"
---
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# Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution

**Authors:** [Xingyu Lu](https://md.research.elamur.ai/free/en/authors/xingyu-lu/), Yumeng Ma, [Xiang Zhou](https://md.research.elamur.ai/free/en/authors/xiang-zhou/), Shengli Gan, Guiying Deng, Yang Wen and [Yanbing Liu](https://md.research.elamur.ai/free/en/authors/yanbing-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1855/) · [PDF](https://aclanthology.org/2026.findings-acl.1855.pdf) · [DOI 10.18653/v1/2026.findings-acl.1855](https://doi.org/10.18653/v1/2026.findings-acl.1855) · [OpenAlex](https://openalex.org/W7166831532)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This work addresses attributing a text either to a human or to the specific LLM that wrote it. Likelihood-based detectors such as perplexity or curvature scores, and fine-tuned semantic classifiers, both lose reliability on short texts, derivative content, and cross-domain data. The authors observe that LLMs keep stable, topic-independent writing habits ("style inertia"). Building on this, they propose Stylometric-Semantic LLM Attribution (SSLA), which builds explicit stylistic profiles from three kinds of features:
- static linguistic metrics drawn from dependency parsing and lexical statistics;
- a rigidity feature computed by having a probe LLM rewrite the text;
- interpretable syntactic n-grams (SN-Grams) taken from dependency trees.

An attention-based dual-path module merges these signals with RoBERTa contextual embeddings. On text from six LLM families across domains including Wikipedia and essays, SSLA reaches 95.6% Macro-F1 on Wikipedia, outperforming baselines such as OTBDetector. It also stays robust when models are pushed into a shared persona. Its feature-level explanations make it well suited to offline forensic attribution where transparency matters.

## Key points
- The static linguistic vector has six dimensions, including average dependency depth and distance, Type-Token Ratio, and sentence length. It is combined with the rewrite-based feature and SN-Grams.
- Macro-F1 is 0.956 ± 0.011 for standard attribution over 6 LLMs. In an extreme adversarial setting, where 4 SOTA LLMs are forced into an identical persona, it remains 0.952.
- Replacing the default rewriter, DeepSeek-V3, with Gemini-1.5-Flash leaves Average F1 almost unchanged (0.954 vs. 0.956). This suggests the rigidity signal does not depend on the choice of probe.
- In ablations, attention fusion generalizes better than simply concatenating RoBERTa and SN-Gram features, especially on Wikipedia. SHAP analysis shows SN-Gram and lexical-density features carry substantial decision weight.
- The authors list several limitations:
  - extra computational cost;
  - reliance on high-quality parsers, which is an obstacle for low-resource languages;
  - evaluation restricted to closed-set attribution;
  - weaker results on texts under 50 tokens.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chongqing University of Posts and Telecommunications](https://md.research.elamur.ai/free/en/universities/chongqing-university-of-posts-and-telecommunications/) — University, Chongqing, China · 2 works · 1 citation

Also on the byline: [Chongqing Medical University](https://ror.org/017z00e58).

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## Cite

Cite as: Xingyu Lu, Yumeng Ma, Xiang Zhou, Shengli Gan, Guiying Deng, Yang Wen and Yanbing Liu. 2026. Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lu2026synergizing,
  title = {{Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution}},
  author = {Xingyu Lu, Yumeng Ma, Xiang Zhou, Shengli Gan, Guiying Deng, Yang Wen and Yanbing Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1855},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1855}
}
```

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*Updated 1 November 2026*