---
title: "Ranking Human and LLM Texts Using Locality Statistics (2026)"
description: "This paper offers a lightweight, transparent alternative drawn from computer-memory analysis. 98.6% accuracy. 34 references."
authors: ["Yiyang Wang", "Chen Ding", "Hangfeng He"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.283/"
doi: "10.18653/v1/2026.findings-eacl.283"
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/wang-2026-ranking-human-llm-texts-locality/"
---
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# Ranking Human and LLM Texts Using Locality Statistics

**Authors:** [Yiyang Wang](https://md.research.elamur.ai/free/en/authors/yiyang-wang/), Chen Ding and [Hangfeng He](https://md.research.elamur.ai/free/en/authors/hangfeng-he/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 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-eacl.283/) · [PDF](https://aclanthology.org/2026.findings-eacl.283.pdf) · [DOI 10.18653/v1/2026.findings-eacl.283](https://doi.org/10.18653/v1/2026.findings-eacl.283) · [OpenAlex](https://openalex.org/W7140117773)

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

## Summary
Most detectors of machine-generated text rely on large transformer models, which are costly to run and hard to interpret. This paper offers a lightweight, transparent alternative drawn from computer-memory analysis. It uses locality statistics, which measure how far apart repeated tokens occur rather than how often they appear. The authors adapt Data Movement Distance (DMD), a sum of square-rooted Reuse Distances, into a normalized text metric called nDMD. The key addition assigns "imaginary" reuse distances to the first occurrence of each token. This lets rarely repeated words contribute and makes documents of different lengths comparable. A per-token variant, T-nDMD, serves as classifier input.

The method was tested on the English part of M4 (five domains, six LLMs) and on GenAIDetect shared task 1a. Human writing consistently shows higher and more variable nDMD than LLM output. With logistic regression, T-nDMD matches or beats bag-of-words features, and combining the two works best. The approach runs on ordinary CPUs and can also expose generation collapse, which makes it a useful interpretable complement to neural detectors.

## Key points
- **Effect size:** Human text has significantly higher nDMD than LLM text in every English M4 subset (p \< 0.001). The only exception is the ArXiv–BLOOMz pair, which is explained by short abstracts and BLOOMz degenerating into repetitive output.
- **Design choice:** Of six reuse-distance variants, square-root distances with imaginary RDs for first occurrences separated the classes best. They reached 88.6% mean accuracy, versus 85.3–87.8% for the alternatives.
- **Controlled setting (single model, single domain, 7:3 split, 20 seeds, Llama 3 tokenizer):**
  - Bag-of-words (BoW): 98.6% accuracy
  - T-nDMD: 98.9%
  - Combined: 99.3%
  - Subword tokenizers (Llama 3, GPT-2) beat word-level ones (SpaCy, NLTK).
- **Open-domain setting (GenAIDetect task 1a):**

  | Features | Macro-F1 | Accuracy |
  |---|---|---|
  | BoW | 63.1 | 66.3 |
  | T-nDMD | 65.1 | 67.8 |
  | Combined | 66.8 | 69.0 |

- **Code:** Released on GitHub (YYWmus/locality-metrics-for-text). Fixed-window analysis also shows that nDMD can diagnose degenerate repetitive LLM generation.

*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

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## Cite

Cite as: Yiyang Wang, Chen Ding and Hangfeng He. 2026. Ranking Human and LLM Texts Using Locality Statistics. Findings of the Association for Computational Linguistics: EACL 2026.

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

```bibtex
@inproceedings{wang2026ranking,
  title = {{Ranking Human and LLM Texts Using Locality Statistics}},
  author = {Yiyang Wang, Chen Ding and Hangfeng He},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.283},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.283}
}
```

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*Updated 2 November 2026*