As AI continues to expand into various sectors, one of the most challenging yet crucial applications is legal document analysis. Arabic language presents unique hurdles for AI models based on Large Language Models (LLMs) due to its rich linguistic features, right-to-left script, and regional dialects. These complexities make it difficult for generic AI models to effectively process contracts, assess compliance risks, or extract legal insights with accuracy. Moreover, there are some key challenges with adapting current LLMs that are trained on high-resourced datasets like English with other languages like Arabic [1]:

• Unnatural Text Structure: The LLM may generate Arabic content with awkward sentence structures that don't reflect natural Arabic writing patterns.
• Poor Inter-Sentence Coherence: Generated Arabic text by the model may lack proper connective elements between sentences, resulting in disjointed content that doesn't flow naturally.
• Translation Artifacts: The Arabic outputs often read like direct translations from English rather than authentic Arabic, suggesting limitations in the model's understanding of language-specific expression.
• Safety Calibration Requirements: Initial testing revealed the original model produced harmful content in Arabic at high rates (89-90%), requiring specific mitigation strategies to bring this down to safer levels.
• Dataset Representation Imbalance: Arabic, like many non-English languages, suffers from underrepresentation in training data, with research showing that 73% of popular instruction datasets remain primarily English-focused. 

Moreover, recently Cohere released a new model, C4AI Command R7B Arabic [2] which is an open-source 7 billion parameter model specifically designed for Modern Standard Arabic (MSA) and English. This research model delivers exceptional performance on enterprise-critical capabilities including following instructions, controlling response length, retrieval-augmented generation, and maintaining the appropriate language context. It demonstrates strong general knowledge and deep understanding of Arabic language and cultural nuances. At Perle, we are reshaping how organizations approach data labeling, annotation, and machine learning (ML) for AI. Through this demo, we illustrate how we are harnessing AI particularly LLMs for Arabic legal document analysis.

The Perle Arabic Legal AI Demo: Unlocking the Potential of Arabic Legal AI

When it comes to Arabic legal data, the stakes are higher due to two main factors:

• Critical Accuracy: Even a slight misinterpretation of a clause can lead to significant legal consequences. Achieving this level of precision in Arabic requires deep understanding of both legal context and language.
• Data Availability: Arabic legal documents are often less abundant in structured datasets, making AI development and benchmarking in this field particularly challenging.

Our Arabic legal AI demo offers a comprehensive, end-to-end experience of how Perle builds powerful AI systems for Arabic language document understanding. This demonstration provides insight into how we use AI models specifically for the nuances of Arabic legal contracts.

The Key features of the demo include:

• Document summary: For a given document the system provides a summary of the key points in the document including:

1. Precise analysis of text from various parts of the document
2. Identification of main sections and clauses in the contract
3. Creation of a comprehensive and structured summary covering:
   
   • Main parties and purpose of the contract
   • Key obligations and responsibilities
   • Financial terms and conditions
   • Quality and implementation requirements
   • Dispute resolution and termination procedures
   • Any special terms or provisions

• AI-Powered Q&A: Users can ask document-related questions in Arabic and receive precise, contextually relevant answers, enabling quicker decision-making and risk assessment.
• Question similarity Graph: The user can view a knowledge graph based on the similarity of previous questions. This visualization helps users understand which parts of the document generate the most interest and are frequently queried through the QA system.

The following figure shows the sample QA from the user and the system given a question from the user where the question is around the official language used in the given contract:

The following figure also shows the question similarity graph. When the user hovers over each question, similar questions appear closer to each other, and thicker connections between the questions indicate stronger similarity:

By offering a hands-on experience of these tools, the demo helps legal professionals visualize how Perle can transform their workflow and efficiency when dealing with Arabic legal documents.

These are the critical considerations that we are taking into account for building the tool:

1. Pool of native experts for benchmarking: Our benchmarking strategy centers on assembling a diverse team of professionals with native Arabic fluency. This team operates within a multi-tiered review framework that combines legal and linguistic expertise to ensure comprehensive evaluation. Moreover, inter-annotator agreement metrics maintain evaluation consistency. A continuous feedback loop system captures expert insights, enabling ongoing refinement of our models and annotation guidelines to address the nuanced challenges of Arabic legal language processing.
2. Initiative on collecting legal documents dataset: We prioritize building a balanced corpus that represents the diversity of Arabic-speaking legal systems, with rigorous quality control processes ensuring both linguistic accuracy and legal authenticity. 
3. Rigorous benchmarking assessment criteria for QA system based on:
   
   • Formatting: Evaluation of text layout, paragraph structure, and proper handling of Arabic-specific formatting requirements
   • Spelling and Grammar: Assessment of linguistic accuracy including proper use of diacritics, case endings, and legal terminology
   • Instruction Following: Measurement of the system's ability to adhere to specific query parameters and legal context
   • Verbosity: Evaluation of response length appropriateness, balancing comprehensiveness with conciseness
   • Truthfulness: Verification of factual accuracy and legal correctness of generated responses
   • Missing Parts: Identification of critical information omissions in system outputs
   • Overall Quality: Holistic assessment of usefulness, relevance, and practical applicability in legal workflows
4. Examining multiple AI models for the best foundational model selection: Our model selection process involves rigorous comparative analysis of leading LLMs including GPT models, Llama, and Aya, evaluating their respective strengths in handling Arabic legal content across diverse document types. We specifically focus on Aya's multilingual capabilities designed for non-English languages, comparing its performance against general-purpose models like GPT and Llama that have been adapted for Arabic. We are also considering building our foundational model using the dataset that we are collecting to adapt the models according to legal terminology.

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Join Us in Transforming Legal AI

At Perle, our goal is to not only provide AI tools for legal professionals but to set new standards in the field of legal AI. Our commitment to high-quality data annotation, deep understanding of Arabic legal language, and expert-driven models positions us at the forefront of AI-driven legal technology.

Ready to see how AI can transform your approach to Arabic legal document analysis? Try our demo today and experience firsthand how Perle is shaping the future of legal AI.

References

[1] Üstün, Ahmet, et al. "Aya model: An instruction finetuned open-access multilingual language model." arXiv preprint arXiv:2402.07827 (2024).

[2] Cohere team, C4AI Command R7B Arabic model