How a National AI Lab used Collinear’s high-quality multilingual data packs to improve Arabic & English Model Alignment
“Collinear was instrumental in making our models state-of-the-art in Arabic and English across all sizes (7B–70B). Their judges-driven preference curation made post-training alignment seamless.”
A leading research lab set out to build a family of foundation models optimized for both Arabic and English. Committed to responsible AI, they aimed to deliver enterprise-grade solutions that combined best-in-class performance with rigorous safety standards.
“Collinear was instrumental in making our models state-of-the-art in Arabic and English across all sizes (7B–70B). Their judges-driven preference curation made post-training alignment seamless.”
The Challenge: A Critical Gap in High-Quality Arabic Alignment Data
These models faced a critical challenge: ensuring safe, reliable responses across diverse scenarios in both Arabic and English. As the first Arabic-optimized foundation model of its kind, these models significantly outperformed global alternatives on native Arabic tasks, offering transformative potential for government services, educational platforms, and commercial applications.
However, deploying these models in sensitive contexts presented the lab with challenges that conventional approaches couldn't adequately address:
- Navigating the complex interplay between Islamic values and AI outputs in ways Western safety frameworks failed to capture
- Ensuring consistent safety across both Modern Standard Arabic and regional dialects while maintaining exceptional Arabic language performance
- Protecting against culturally-specific manipulation attempts targeting institutions and values
- Meeting stringent regulatory requirements for AI systems supporting critical Saudi infrastructure and service platforms
They needed a scalable way to generate culturally-aware, bilingual data across dozens of categories and dialects, so they could close performance gaps and enable safe, high-quality deployment across public-sector applications.
The Solution: Collinear’s Multilingual Data Engine for Post-Training
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Partnering with Collinear AI provided the frontier lab with a specialized framework designed to systematically evaluate and enhance model safety:
- Custom Data Taxonomy: Collinear collaborated with the lab to define 30+ semantic categories and tailored data recipes for Arabic and English, ensuring coverage across dialects, values, and regulatory constraints.
- Synthetic Preference Data Generation: Leveraged Collinear’s synthetic data curator to create targeted training data, directly addressing identified safety weaknesses.
- Cross-lingual Safety Evaluation: Benchmarked their model against leading commercial open and closed-source models, pinpointing language-specific vulnerabilities.
- Direct Preference Optimization (DPO): Implemented precision fine-tuning to rapidly enhance model alignment in specific high-risk categories.
- Comprehensive Re-evaluation: Conducted a thorough assessment on held-out test sets to validate improvements and ensure deployment readiness
The Results: Setting New Standards for Multilingual AI Alignment & Safety
The partnership with Collinear produced transformative outcomes:
✔ Over 10k+ model failure modes proactively identified across languages in pre-production, enabling immediate targeted remediation.
✔ Consistent cross-lingual safety performance, achieving approximately 1/7th the failure rate of comparable open source Llama models, marking a dramatic improvement in Arabic safety performance over baseline models.
✔ First-of-its-kind Arabafety vulnerability mapping creating a proprietary security advantage for Saudi AI development.
✔ Comprehensive model benchmarking delivered detailed scorecards, enabling the lab to transparently demonstrate robust safety and reliability for enterprise deployments.
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Most impressively, while the baseline Llama models demonstrated a concerning 72.6% failure rate when responding to Arabic-language manipulation attempts, the models achieved a minor fraction of that—proving that their investment in Arabic-optimized AI delivers not just performance advantages but substantial safety improvements.
Collinear’s rigorous methodology and innovative approach allowed the lab’s suite of models to meet enterprise-grade safety and reliability standards, significantly enhancing trust and adoption potential.
Looking Ahead: Saudi Arabia's AI Sovereignty Secured
This frontier labs collaboration with Collinear has redefined multilingual AI safety standards, positioning them as a model of responsible AI innovation. Together, we are pioneering the future of safe, reliable, and responsible multilingual AI—setting benchmarks for excellence and trust across industries and geographies.
With the safety foundation now established for their model, this frontier research lab and Collinear have already begun extending this effective framework to the more powerful and agentic model variants—ensuring that as their AI capabilities expand, its model safety leadership advances in parallel.
Need high-quality training data? Discover how Collinear’s curated data packs accelerate post-training alignment.
Stop guessing if your data is good enough for production. Book a demo to see how Collinear builds high-signal, multilingual data packs tailored to your models and domains.
A national, Middle-East state-backed AI initiative focused on building full‑stack sovereign AI infrastructure, from hyperscale data centers and cloud platforms to large language models and applied AI services, to accelerate economic diversification, strengthen data sovereignty, and position themselves as a global leader in artificial intelligence.
A leading research lab set out to build a family of foundation models optimized for both Arabic and English. Committed to responsible AI, they aimed to deliver enterprise-grade solutions that combined best-in-class performance with rigorous safety standards.
“Collinear was instrumental in making our models state-of-the-art in Arabic and English across all sizes (7B–70B). Their judges-driven preference curation made post-training alignment seamless.”
The Challenge: A Critical Gap in High-Quality Arabic Alignment Data
These models faced a critical challenge: ensuring safe, reliable responses across diverse scenarios in both Arabic and English. As the first Arabic-optimized foundation model of its kind, these models significantly outperformed global alternatives on native Arabic tasks, offering transformative potential for government services, educational platforms, and commercial applications.
However, deploying these models in sensitive contexts presented the lab with challenges that conventional approaches couldn't adequately address:
- Navigating the complex interplay between Islamic values and AI outputs in ways Western safety frameworks failed to capture
- Ensuring consistent safety across both Modern Standard Arabic and regional dialects while maintaining exceptional Arabic language performance
- Protecting against culturally-specific manipulation attempts targeting institutions and values
- Meeting stringent regulatory requirements for AI systems supporting critical Saudi infrastructure and service platforms
They needed a scalable way to generate culturally-aware, bilingual data across dozens of categories and dialects, so they could close performance gaps and enable safe, high-quality deployment across public-sector applications.
The Solution: Collinear’s Multilingual Data Engine for Post-Training
Partnering with Collinear AI provided the frontier lab with a specialized framework designed to systematically evaluate and enhance model safety:
- Custom Data Taxonomy: Collinear collaborated with the lab to define 30+ semantic categories and tailored data recipes for Arabic and English, ensuring coverage across dialects, values, and regulatory constraints.
- Synthetic Preference Data Generation: Leveraged Collinear’s synthetic data curator to create targeted training data, directly addressing identified safety weaknesses.
- Cross-lingual Safety Evaluation: Benchmarked their model against leading commercial open and closed-source models, pinpointing language-specific vulnerabilities.
- Direct Preference Optimization (DPO): Implemented precision fine-tuning to rapidly enhance model alignment in specific high-risk categories.
- Comprehensive Re-evaluation: Conducted a thorough assessment on held-out test sets to validate improvements and ensure deployment readiness
The Results: Setting New Standards for Multilingual AI Alignment & Safety
The partnership with Collinear produced transformative outcomes:
✔ Over 10k+ model failure modes proactively identified across languages in pre-production, enabling immediate targeted remediation.
✔ Consistent cross-lingual safety performance, achieving approximately 1/7th the failure rate of comparable open source Llama models, marking a dramatic improvement in Arabic safety performance over baseline models.
✔ First-of-its-kind Arabafety vulnerability mapping creating a proprietary security advantage for Saudi AI development.
✔ Comprehensive model benchmarking delivered detailed scorecards, enabling the lab to transparently demonstrate robust safety and reliability for enterprise deployments.
Most impressively, while the baseline Llama models demonstrated a concerning 72.6% failure rate when responding to Arabic-language manipulation attempts, the models achieved a minor fraction of that—proving that their investment in Arabic-optimized AI delivers not just performance advantages but substantial safety improvements.
Collinear’s rigorous methodology and innovative approach allowed the lab’s suite of models to meet enterprise-grade safety and reliability standards, significantly enhancing trust and adoption potential.
Looking Ahead: Saudi Arabia's AI Sovereignty Secured
This frontier labs collaboration with Collinear has redefined multilingual AI safety standards, positioning them as a model of responsible AI innovation. Together, we are pioneering the future of safe, reliable, and responsible multilingual AI—setting benchmarks for excellence and trust across industries and geographies.
With the safety foundation now established for their model, this frontier research lab and Collinear have already begun extending this effective framework to the more powerful and agentic model variants—ensuring that as their AI capabilities expand, its model safety leadership advances in parallel.
Need high-quality training data? Discover how Collinear’s curated data packs accelerate post-training alignment.
Stop guessing if your data is good enough for production. Book a demo to see how Collinear builds high-signal, multilingual data packs tailored to your models and domains.
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