---
title: "Verifiable LLM-Generated Text Detection via Projected"
description: "The paper targets detection of machine-generated text (MGT). 67 references. Among them: 0."
authors: ["Ruochong Xiong", "Qien Li", "Wangwang Lian", "Yulong Wan", "Hanlin Xue", "Zhouxing Tan", "Han Yang", "Fengyu Lu", "Junfei Liu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.638/"
doi: "10.18653/v1/2026.acl-long.638"
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/xiong-2026-verifiable-llm-generated-text-detection/"
---
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# Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions

**Authors:** Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu and Junfei Liu · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.638/) · [PDF](https://aclanthology.org/2026.acl-long.638.pdf) · [DOI 10.18653/v1/2026.acl-long.638](https://doi.org/10.18653/v1/2026.acl-long.638) · [OpenAlex](https://openalex.org/W7166882480)

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

## Summary
The paper targets detection of machine-generated text (MGT). Current detectors rely on proxy-model probabilities or a single semantic signal, which brings distribution misalignment, high compute costs, and little transparency. The authors found that, within a projected space combining semantics and syntax, MGT centroids move in one consistent direction away from human-written text (HWT) across many local syntactic configurations.

Their framework, ProSSD, works in three steps:
- **Projection:** RoBERTa embeddings are mapped into a compact, label-aware subspace using supervised subspace projection (SSP) rather than PCA.
- **Modeling:** semantic distributions are modeled conditioned on syntactic structure.
- **Scoring:** texts are scored with a modified Mahalanobis distance, derived from a likelihood ratio test and weighted by Wasserstein distance.

No task-specific fine-tuning is needed. On DetectRL, which includes models such as GPT-5.1, ProSSD performs strongly in cross-domain, cross-model, and adversarial settings. It is also fast, memory-light, and data-efficient. Because every decision is a verifiable statistical test, the method matters for forensics and content moderation.

## Key points
- **Efficiency:** about 0.08 s per sample and 1.35 GB of GPU memory, 74.62% less than the runner-up. Its reference AUROC is 99.97, compared with 99.59 for RepreGuard and 92.52 for Binoculars.
- **Ablation:** replacing SSP with PCA lowers XSum AUC from 99.21 to 94.75. Dropping the human/machine contrast lowers Review AUC to 88.70.
- **Settings and data needs:** the best N-gram window is k = 4. Performance plateaus after roughly 400 reference samples and remains strong on short texts of 20–80 tokens.
- **Interpretability findings:** according to the authors, MGT shows a consistent, directional shift in its semantics as well as a collapse of its semantic variety. Formal domains rely on logical connectives as cues, while subjective texts rely on pronouns and adjectives.
- **Limitations and availability:** heavily polished or hybrid human-machine text is not handled well, and source attribution is not supported. Code and data are at github.com/RuoChoXio/ProSSD.

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

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- [Central South University](https://md.research.elamur.ai/free/en/universities/central-south-university/) — University, Changsha, China · 23 works · 657 citations
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## Cite

Cite as: Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu and Junfei Liu. 2026. Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{xiong2026verifiable,
  title = {{Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions}},
  author = {Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu and Junfei Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.638},
  url = {https://doi.org/10.18653/v1/2026.acl-long.638}
}
```

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*Updated 2 November 2026*