As generative AI expands into regulated industries, traditional models often fall short-especially when applied to complex languages like Arabic.AI development is moving beyond just bigger models. The key to progress today is high-quality, richly annotated data.

Perle's new white paper explores key challenges large language models face when working with Arabic text, including dialectal nuance and domain-specific vocabulary. It uses legal documents as a case study to illustrate these issues-highlighting how structured data, native expert input, and task-specific fine-tuning can improve performance in complex language contexts.

This white paper covers:

Why Arabic-language tasks challenge most general-purpose LLMs

Our pipeline for clause extraction, contract summarization, and Arabic legal Q&A

Benchmarks across GPT-4, LLaMA, Aya, and C4AI's new Arabic model

A preview of our interactive tools: semantic Q&A and query similarity graph

Download the paper to see how high-quality, domain-specific data is unlocking the next frontier of legal AI in Arabic.