---
title: "ALHD: A Large-Scale and Multigenre — Khairallah 2025"
description: "Arabic has had no large public resource for telling human-written text apart from text produced by LLMs."
authors: ["Ali Khairallah", "Arkaitz Zubiaga"]
year: 2025
venue: "arXiv:2510.03502"
source: "https://arxiv.org/abs/2510.03502"
doi: "10.48550/arxiv.2510.03502"
arxiv: "2510.03502"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/khairallah-2025-alhd-large-scale-multigenre-benchmark/"
---
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# ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection

**Authors:** Ali Khairallah, [Arkaitz Zubiaga](https://md.research.elamur.ai/free/en/authors/arkaitz-zubiaga/) · **Year:** 2025 · **Venue:** arXiv:2510.03502 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2510.03502) · [PDF](https://arxiv.org/pdf/2510.03502) · [DOI 10.48550/arxiv.2510.03502](https://doi.org/10.48550/arxiv.2510.03502) · [arXiv 2510.03502](https://arxiv.org/abs/2510.03502) · [OpenAlex](https://openalex.org/W4414969208)

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

## Summary
Arabic has had no large public resource for telling human-written text apart from text produced by LLMs. Earlier collections were small, used a single generator, or covered only one or two genres, so it was hard to test whether detectors generalize. The authors built ALHD to fill this gap. It is a balanced corpus of more than 400K samples across news, social media and reviews, covering both MSA and dialectal Arabic. The human side comes from six existing Arabic datasets. The machine side was produced by three commercial LLMs. The release includes preprocessing, annotations and fixed splits.

The authors benchmarked classical classifiers, fine-tuned BERT-style models, and prompted LLMs in zero-shot and few-shot modes. Fine-tuned BERT models came out on top, often above 90% accuracy. Classical models formed a weaker but respectable baseline, and prompted LLMs did worst. All model families degraded when tested on a genre they had not been trained on. News was the hardest genre, because generated articles closely mimic human journalistic style. ALHD therefore gives Arabic detection research a shared testbed focused on robustness.

## Key points
- ALHD has over 400K balanced human/LLM samples in three genres (news, social media, reviews), covering MSA and some dialectal Arabic.
- The human texts come from SANAD, ANAD, MDAT, MASC, HARD and BRAD.
- Generated texts came from GPT-3.5-turbo, Gemini 2.5 and Command-R, called through official APIs with default settings.
- Fine-tuned BERT models beat traditional ML and zero-/few-shot LLM prompting.
- Cross-genre transfer is weak, especially when news is the target genre.
- The authors ran 540 reproducible experiments.
- The data is on Zenodo under CC BY-NC 4.0, and the code is on GitHub under MIT.

## Interesting ideas
- Train on one genre and test on another, so that apparent in-genre accuracy cannot hide a model that has only learned genre-specific cues. — For Mova (learn): Evaluate the parser and frame extractor on held-out genres (e.g., train on news-like text, test on fables) to measure whether rules and models truly generalize.
- Pair existing human-only corpora with matched machine-generated counterparts instead of collecting everything from scratch, keeping classes balanced per source. — For Mova (learn): Build balanced silver sets from existing treebank sentences plus LLM-written analogues, as a negative control for whether witness parsers favour machine-style text.
- Generate machine text with default API parameters, so the data reflects ordinary user behaviour rather than a tuned adversary. — For Mova (learn): When using LLM teacher tiers to produce training text, record and fix the generation settings so the resulting data distribution is known and reproducible.
- The finding that news is hardest suggests surface style alone is a fragile signal once generators imitate a register well. — For Mova (back): Rather than relying on style, check regenerated text against the meaning graph (who did what to whom) to judge fidelity.
- Compare cheap classical baselines, fine-tuned encoders and prompted LLMs side by side under identical splits, so the relative value of each tier is clear. — For Mova (learn): Benchmark Mova's explainable pipeline against a fine-tuned encoder and a prompted LLM on the same school-test splits, so its gains or gaps are measured honestly.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

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

```bibtex
@misc{khairallah2025alhd,
  title = {{ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection}},
  author = {Khairallah, Ali and Zubiaga, Arkaitz},
  year = {2025},
  doi = {10.48550/arxiv.2510.03502},
  eprint = {2510.03502},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2510.03502}
}
```

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