---
title: "AraHalluEval — paper by Alansari and Luqman (2025)"
description: "Most work on LLM hallucination targets English. 44 references, cited by 6 papers here. Among them: 3."
authors: ["Aisha Alansari", "Hamzah Luqman"]
year: 2025
venue: "Proceedings of The Third Arabic Natural Language Processing Conference"
source: "https://aclanthology.org/2025.arabicnlp-main.12/"
doi: "10.18653/v1/2025.arabicnlp-main.12"
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/alansari-2025-arahallueval-fine-grained-hallucination-evaluation/"
---
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# AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs

**Authors:** [Aisha Alansari](https://md.research.elamur.ai/free/en/authors/aisha-alansari/) and [Hamzah Luqman](https://md.research.elamur.ai/free/en/authors/hamzah-luqman/) · **Year:** 2025 · **Venue:** [Proceedings of The Third Arabic Natural Language Processing Conference](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2025](https://md.research.elamur.ai/free/en/venues/arabicnlp/2025/)) · **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/2025.arabicnlp-main.12/) · [PDF](https://aclanthology.org/2025.arabicnlp-main.12.pdf) · [DOI 10.18653/v1/2025.arabicnlp-main.12](https://doi.org/10.18653/v1/2025.arabicnlp-main.12) · [OpenAlex](https://openalex.org/W4416037696)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/alansari-2025-arahallueval-fine-grained-hallucination-evaluation/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most work on LLM hallucination targets English. This paper asks how often Arabic-specific, multilingual and reasoning-oriented LLMs produce false or unfaithful content when they write in Arabic. The authors evaluate 12 models zero-shot on two generation tasks. For generative question answering (GQA) they use 300 sampled questions from TyDiQA-GoldP-AR. For abstractive summarization they use 100 articles from the Arabic XLSum test set. Humans annotate every output with a taxonomy of 12 fine-grained hallucination indicators, split into factuality errors (disagreement with the real world) and faithfulness errors (disagreement with the input or with the response itself). Factual errors outnumber faithfulness errors for every model and both tasks. The Arabic-pretrained Allam beats the multilingual models and comes close to several reasoning models. A separate test on a manually translated TruthfulQA shows that reasoning models hallucinate more often when they answer in Arabic than in English. The work supplies a diagnostic scheme and an annotated dataset for a language with few hallucination benchmarks.

## Key points
- The 12 models fall into three groups: 4 Arabic-pretrained (including Allam), 4 multilingual (including Qwen2.5) and 4 reasoning-based (DeepSeek-r1, GPT-4o, GPT-o3, QwQ).
- GQA outputs are scored with nine indicators. Examples include named-entity error, value error (dates, ages), factual contradiction and response conflict (the answer contradicts itself).
- On GQA, GPT-4o has the lowest factuality and faithfulness scores (0.364 / 0.235) and QwQ the highest (0.779 / 0.471). Allam reaches an average of 0.382, comparable to DeepSeek-r1 and QwQ. On summarization, GPT-4o averages 0.105 and QwQ 0.730.
- Arabic models produce fewer factual GQA hallucinations than multilingual ones, and a Mann-Whitney U test confirms the difference (U = 649,023.5, p ≈ 8.19e-6).
- On TruthfulQA, every model hallucinates more in Arabic, for example GPT-o3 at 0.649 vs 0.548. A paired t-test gives t = 3.37, p ≈ 0.028.

## Interesting ideas
- The taxonomy separates errors against world knowledge from errors against the given input or the response's own earlier statements, so a system can learn which source of truth it violated.
- Errors are labeled by type, such as wrong named entity, wrong number or date, or invented fact, which gives a much finer diagnosis than a single correct/incorrect verdict.
- A "response conflict" check flags outputs that contradict themselves, for instance an event dated before its own launch, without needing any external ground truth.
- Running the same benchmark as parallel translated items lets the authors attribute a performance gap to language alone, confirmed by a paired significance test.
- The manually annotated hallucination labels are offered as training material for automatic hallucination detectors, so the evaluation effort becomes reusable supervision.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): The world-of-text builder could tag each inconsistency by its source: a clash with compiled knowledge (Wikidata/WordNet), a clash with the input text, or a clash between two states inside the same world.
- **I2** (answer): School-test and fable-QA readers could report the failure type for each wrong answer (entity, quantity, unsupported claim), showing which upstream stage needs repair.
- **I3** (back): The text→graph→text compressor could re-parse its own generated text and test the rebuilt graph for internal contradictions in time order or values before emitting it.
- **I4** (learn): Evaluation with negative controls could use paired items (paraphrase vs original, or translated vs source) with paired tests to check whether a reported gain is real.
- **I5** (learn): Human or LLM-teacher error labels on Mova's outputs could be stored with error types and used to induce repair rules or train a consistency checker.

*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

- [King Fahd University of Petroleum and Minerals](https://md.research.elamur.ai/free/en/universities/king-fahd-university-of-petroleum-and-minerals/) — University, Dhahran, Saudi Arabia · 52 works · 4,666 citations

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🧭 Topics (4): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works

## Cite

Cite as: Aisha Alansari and Hamzah Luqman. 2025. AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs. Proceedings of The Third Arabic Natural Language Processing Conference.

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

```bibtex
@inproceedings{alansari2025arahallueval,
  title = {{AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs}},
  author = {Aisha Alansari and Hamzah Luqman},
  booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference},
  year = {2025},
  doi = {10.18653/v1/2025.arabicnlp-main.12},
  url = {https://doi.org/10.18653/v1/2025.arabicnlp-main.12}
}
```

- [← previous Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages](https://md.research.elamur.ai/free/en/papers/chen-2025-advancing-mathematical-reasoning-language-models/)
- [next → BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering](https://md.research.elamur.ai/free/en/papers/zhang-2025-belle-bi-level-multi-agent/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/9/)

## References

44 references, 28 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*