ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection

• URL: http://arxiv.org/abs/2510.03502v2
• Date: Tue, 21 Oct 2025 20:12:50 GMT
• Title: ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection
• Authors: Ali Khairallah, Arkaitz Zubiaga,
• Abstract summary: We introduce ALHD, the first large-scale comprehensive Arabic dataset explicitly designed to distinguish between human- and LLM-generated texts.<n> ALHD spans three genres (news, social media, reviews), covering both MSA and dialectal Arabic.<n>We provide rigorous preprocessing, rich annotations, and standardized balanced splits.
• Score: 9.866690791407464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ALHD, the first large-scale comprehensive Arabic dataset explicitly designed to distinguish between human- and LLM-generated texts. ALHD spans three genres (news, social media, reviews), covering both MSA and dialectal Arabic, and contains over 400K balanced samples generated by three leading LLMs and originated from multiple human sources, which enables studying generalizability in Arabic LLM-genearted text detection. We provide rigorous preprocessing, rich annotations, and standardized balanced splits to support reproducibility. In addition, we present, analyze and discuss benchmark experiments using our new dataset, in turn identifying gaps and proposing future research directions. Benchmarking across traditional classifiers, BERT-based models, and LLMs (zero-shot and few-shot) demonstrates that fine-tuned BERT models achieve competitive performance, outperforming LLM-based models. Results are however not always consistent, as we observe challenges when generalizing across genres; indeed, models struggle to generalize when they need to deal with unseen patterns in cross-genre settings, and these challenges are particularly prominent when dealing with news articles, where LLM-generated texts resemble human texts in style, which opens up avenues for future research. ALHD establishes a foundation for research related to Arabic LLM-detection and mitigating risks of misinformation, academic dishonesty, and cyber threats.

Related papers

• How Do LLM-Generated Texts Impact Term-Based Retrieval Models? [76.92519309816008]
  This paper investigates the influence of large language models (LLMs) on term-based retrieval models.<n>Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes.<n>Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries.
  arXiv  Detail & Related papers  (2025-08-25T06:43:27Z)
• Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text [4.753395401707132]
  DA-MTL is a multi-task learning framework that simultaneously addresses text detection and authorship attribution.<n>We evaluate DA-MTL on nine datasets and four backbone models, demonstrating its strong performance across multiple languages.
  arXiv  Detail & Related papers  (2025-08-19T18:23:30Z)
• The Arabic AI Fingerprint: Stylometric Analysis and Detection of Large Language Models Text [0.05399757380241794]
  Large Language Models (LLMs) have achieved unprecedented capabilities in generating human-like text.<n>This paper presents a comprehensive investigation of Arabic machine-generated text.<n>We develop BERT-based detection models that achieve exceptional performance in formal contexts.
  arXiv  Detail & Related papers  (2025-05-29T09:24:00Z)
• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs)<n>We find that fine-tuning text embedding models on LLM-generated texts yields excellent classification accuracy.<n>We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• ViLBias: Detecting and Reasoning about Bias in Multimodal Content [6.710013984078675]
  ViLBias is a framework for detecting and reasoning about bias in multimodal news.<n> dataset comprises 40,945 text--image pairs from diverse outlets.<n>Results show that images alongside text improves detection accuracy by 3--5%.
  arXiv  Detail & Related papers  (2024-12-22T15:05:30Z)
• When Text Embedding Meets Large Language Model: A Comprehensive Survey [17.263184207651072]
  This survey focuses on the interplay between large language models (LLMs) and text embeddings.<n>It offers a novel and systematic overview of contributions from various research and application domains.<n>Building on this analysis, we outline prospective directions for the evolution of text embedding.
  arXiv  Detail & Related papers  (2024-12-12T10:50:26Z)
• Robust Detection of LLM-Generated Text: A Comparative Analysis [0.276240219662896]
  Large language models can be widely integrated into many aspects of life, and their output can quickly fill all network resources. It becomes increasingly important to develop powerful detectors for the generated text. This detector is essential to prevent the potential misuse of these technologies and to protect areas such as social media from the negative effects.
  arXiv  Detail & Related papers  (2024-11-09T18:27:15Z)
• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT [9.682499180341273]
  Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges.<n>We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English.<n>This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• DALD: Improving Logits-based Detector without Logits from Black-box LLMs [56.234109491884126]
  Large Language Models (LLMs) have revolutionized text generation, producing outputs that closely mimic human writing. We present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations.
  arXiv  Detail & Related papers  (2024-06-07T19:38:05Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• ToBlend: Token-Level Blending With an Ensemble of LLMs to Attack AI-Generated Text Detection [6.27025292177391]
  ToBlend is a novel token-level ensemble text generation method to challenge the robustness of current AI-content detection approaches. We find ToBlend significantly drops the performance of most mainstream AI-content detection methods.
  arXiv  Detail & Related papers  (2024-02-17T02:25:57Z)
• Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models [49.74036826946397]
  This study investigates constrained text generation for large language models (LLMs) Our research mainly focuses on mainstream open-source LLMs, categorizing constraints into lexical, structural, and relation-based types. Results illuminate LLMs' capacity and deficiency to incorporate constraints and provide insights for future developments in constrained text generation.
  arXiv  Detail & Related papers  (2023-10-25T03:58:49Z)
• MAGE: Machine-generated Text Detection in the Wild [82.70561073277801]
  Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection. We build a comprehensive testbed by gathering texts from diverse human writings and texts generated by different LLMs. Despite challenges, the top-performing detector can identify 86.54% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.
  arXiv  Detail & Related papers  (2023-05-22T17:13:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.