Enterprise AI Analysis
MENARA: Medical Natural Arabic Response Assistant
Dialectal variation presents a major challenge for deploying medical language models in real-world healthcare settings, where patient-clinician communication often occurs in regional vernaculars rather than standardized language forms. This challenge is particularly pronounced in the Arabic-speaking world, where clinical interactions frequently take place in diverse dialects that differ substantially from Modern Standard Arabic. Fine-tuning and maintaining separate models for each dialect is computationally inefficient and difficult to scale, motivating more integrated approaches. In this work, we present MENARA, an Arabic medical language model constructed by merging Egyptian Arabic, Moroccan Darija, and medical-domain specialists through model merging. We extend prior feasibility findings through comprehensive evaluation of cross-dialect performance, medical safety, and cross-lingual knowledge retention. Specifically, we introduce a fine-grained dialect composition analysis to quantify lexical purity and structured code-switching behavior, benchmark against state-of-the-art Arabic LLMs, conduct subject-matter-expert assessment of both dialectal fidelity and medical appropriateness. The results show that model merging preserves core medical competence while enabling robust dialectal adaptation, achieving strong cross-dialect fidelity while substantially reducing storage and deployment overhead compared to maintaining separate models. These findings establish model merging as a potentially practical and resource-efficient paradigm for dialect-aware medical NLP in linguistically fragmented healthcare environments.
Authors: Ahmed Ibrahim, Abdullah Hosseini, Hoda Helmy, Maryam Arabi, Aya AlShareef, Wafa Lakhdhar and Ahmed Serag
Published: 21 April 2026 | Tags: Artificial Intelligence (AI), large language models (LLMs), Natural Language Processing (NLP), model merging, Arabic dialects
Executive Impact
MENARA's model merging approach delivers significant operational efficiencies and enhances clinical communication in linguistically diverse environments.
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Improved Dialectal Fidelity
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Reduced Storage Requirements
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Merging Process Time
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Dialects Supported
Deep Analysis & Enterprise Applications
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Model Merging Strategy
Dialectal Fidelity & Composition
Medical Competence
Deployment Advantages
Leveraging TIES for Unified Specialization
The MENARA project employs TIES (Trim, Elect Sign, & Sign-aware Merge) as its core merging strategy. TIES is chosen for its ability to intelligently consolidate parameter updates from independently fine-tuned models while suppressing conflicting directions. This approach allows MENARA to inherit multiple specializations—such as Egyptian Arabic, Moroccan Darija, and medical domain knowledge—into a single unified architecture, mitigating task interference.
Unlike simpler methods like linear averaging or spherical linear interpolation (SLERP), TIES performs a structured consolidation. It identifies and retains high-signal updates from specialists while resolving directional inconsistencies, which is crucial for balancing linguistic adaptation and core medical knowledge. This ensures robust cross-dialect performance without the computational and logistical burdens of maintaining separate per-dialect models.
Quantifying Dialectal Purity and Cross-Dialect Robustness
MENARA's dialectal fidelity is rigorously evaluated through both LLM-based and human linguistic assessments, demonstrating its ability to conform to the lexical and morphosyntactic norms of target Arabic varieties. The model maintains consistently high linguistic realism across dialects, achieving strong scores without catastrophic degradation.
A lexical composition analysis provides granular insight into dialectal distribution. When prompted in MSA, MENARA exhibits 88% MSA purity. For Egyptian Arabic, 71% Egyptian Arabic content is generated, with natural code-switching to MSA (24%) reflecting real-world usage. In Moroccan Darija, 54% Moroccan Darija is generated, alongside significant MSA (27%) and other languages (11%) content, accurately mirroring the common code-switching behavior with French loanwords in medical contexts.
Ensuring Clinical Safety and Factual Accuracy
Medical competence is a critical evaluation dimension for MENARA. Subject matter experts (SMEs) assessed generated responses for factual accuracy, omission of critical details, and inclusion of appropriate safety caveats on a 1–5 Likert scale. While English outputs were consistently accurate and clinically sound, dialectal responses showed moderate variability, highlighting the challenge of extending deep medical knowledge to low-resource dialects.
Despite this, MENARA (2B parameters) significantly outperformed larger general-purpose Arabic LLMs (e.g., Jais-13B, ALLaM-7B, Fanar-7B) in dialectal fidelity across medical settings. This underscores the value of specialization through merging over scale alone for domain-specific applications, while retaining strong English performance for global clinical literature access.
Resource-Efficient Scalability for Diverse Healthcare Settings
Model merging offers compelling advantages for deployment in resource-constrained healthcare environments. MENARA's TIES-merging process completed in approximately 10 minutes on a single L4 GPU, utilizing 9.3 GB of memory. This resulted in a 67% reduction in storage requirements compared to maintaining separate specialized models.
This lightweight computational footprint makes dialect-aware medical NLP feasible where per-dialect fine-tuning would be impractical due to limited computational resources and data scarcity. It validates merging as an effective strategy for building a single, unified model capable of supporting multiple specialized behaviors, providing a flexible pathway for extending model capabilities without full retraining, and preserving core competencies alongside new specializations.
67%
Reduced Storage Requirements
Model merging substantially reduced the storage overhead compared to maintaining separate specialized models, making dialect-aware medical NLP feasible in resource-constrained environments.
Enterprise Process Flow (MENARA Framework)
Test Data Generation
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Model Merging (TIES)
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LLM-Based Evaluation
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Human-Based Evaluation
| Feature | MENARA | ALLaM-7B | Jais-13B | Fanar-7B |
|---|---|---|---|---|
| Average Fidelity Score (1-5) | 3.68 | 2.93 | 2.84 | 2.55 |
| Egyptian Arabic Fidelity | 3.02 | 2.31 | 2.10 | 1.48 |
| Moroccan Darija Fidelity | 3.12 | 1.72 | 1.88 | 1.25 |
| Parameter Count | 2B | 7B | 13B | 7B |
Case Study: Enabling Cross-Dialect Clinical Communication
Problem: In healthcare, patient-clinician communication often occurs in diverse regional vernaculars rather than standardized language, particularly in the Arabic-speaking world, leading to misinterpretations and potential diagnostic errors.
Solution: MENARA demonstrates the ability to produce coherent and dialectally appropriate responses when the input and output varieties differ (e.g., MSA question → Egyptian Arabic answer, Egyptian Arabic question → Moroccan Darija answer, Moroccan Darija question → MSA response), enabling effective cross-dialect clinical communication.
Outcome: This capability directly addresses real-world communication barriers in multilingual healthcare environments, supporting accurate interpretation of inputs in one dialect and coherent reformulation in another, ultimately improving patient care and safety.
Calculate Your Potential ROI with MENARA
Estimate the efficiency gains and cost savings your organization could achieve by implementing an AI solution like MENARA for specialized language processing.
Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A typical phased approach to integrate MENARA or similar specialized AI solutions into your enterprise.
Phase 01: Discovery & Strategy
Initial assessment of existing language challenges, dialectal needs, data landscape, and definition of key performance indicators (KPIs) for specialized medical NLP.
Phase 02: Model Adaptation & Integration
Customization of MENARA with your specific medical terminology, integration with existing clinical systems, and dialectal refinement using proprietary data where available.
Phase 03: Pilot Deployment & Validation
Controlled rollout in a specific department or use case, comprehensive testing with medical experts, and validation of dialectal fidelity and clinical safety in real-world scenarios.
(Optional)Phase 04: Scaling & Continuous Improvement
Full-scale deployment across the organization, ongoing monitoring of model performance, iterative improvements based on user feedback, and extension to new dialects or medical domains.
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