REVIEW 14 cited by
ALLaM: Large Language Models for Arabic and English
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.
Forward citations
Cited by 14 Pith papers
-
Language Shapes Instruction Hierarchy Compliance in Multilingual LLMs
Instruction-hierarchy compliance in LLMs is asymmetric by language and position, and cross-language conflicts yield systematically higher compliance than same-language ones (Language Boundary Effect).
-
CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals?
Hallucination signals from LLM internals transfer across English–Arabic and Arabic domains for most models, depending on class separability and feature-space language alignment.
-
IslamicTurathBench: A Multi-Task, Multi-Discipline Benchmark for Evaluating Large Language Models on the Islamic Scholarly Tradition (turath)
IslamicTurathBench is a new expert-reviewed Arabic benchmark that tests LLMs on classical Islamic scholarship across seven disciplines, three difficulty tiers, and three task formats.
-
Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation
Arabizi spelling varies systematically across five Arabic dialects, and speakers can often recognize their own dialect's Arabizi, but the recognition result is partly confounded by authors judging their own transcriptions.
-
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text
Code-switching hurts LLM comprehension when non-English tokens enter English text, but inserting English into other languages often improves accuracy; fine-tuning mitigates losses more reliably than prompting.
-
The Arabic AI Fingerprint: Stylometric Analysis and Detection of Large Language Models Text
Arabic text written by LLMs carries detectable stylometric signatures, and fine-tuned XLM-RoBERTa detectors reach near-perfect F1 on academic abstracts but degrade on social media.
-
Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs
The new Fann or Flop benchmark measures LLM comprehension of Arabic poetry through expert-written verse explanations and shows current LLMs perform poorly on interpretive depth.
-
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.
-
RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment
A fully open 518M Arabic-specialized LLM, built by vocabulary injection and standard post-training on Qwen2.5-0.5B, beats same-class multilingual baselines and ships at 398 MB quantized.
-
AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
AraHalluEval introduces a 12-indicator Arabic hallucination taxonomy and finds factual errors dominate, with Allam competitive against reasoning models.
-
Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
Nile-Chat models for dual-script Egyptian Arabic beat strong baselines on newly translated benchmarks, but the evaluation may be inflated by training/eval data overlap and Claude-generated script data.
-
Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model
A compact 1.5B Arabic-English model beats GPT-4o mini only on the authors' own Tarjama-25 benchmark, while trailing large models on standard WMT24++ and IWSLT2017 tests.
-
From Guidelines to Practice: A New Paradigm for Arabic Language Model Evaluation
On a new 490-question Arabic depth dataset, Claude 3.5 Sonnet answered about 30 percent correctly, while GPT-4 answered about 9 percent, showing current models are weak on culturally specialized Arabic knowledge.
-
Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks
Across Arabic, English, and Kannada benchmarks, 4-bit and 8-bit quantization preserves most accuracy while aggressive pruning degrades larger multilingual models more than smaller ones.
Discussion (0). Sign in to comment.