This AI Research from Cohere for AI Compares Merging vs Data Mixing as a Recipe for Building High-Performant Aligned LLMs

Revolutionizing AI with Large Language Models (LLMs) **Understanding the Challenge** Large language models (LLMs) are changing the way we use artificial intelligence. They can perform many tasks in different languages. However, a major challenge is ensuring these models are safe and effective, especially when used in languages and cultures other than English. **Balancing Performance and Safety** The key issue is finding the right balance between performance and safety. Sometimes, LLMs can produce biased or harmful content, especially in languages that have less training data. Current methods often try to improve safety but can end up lowering overall performance. **Innovative Solutions from Cohere AI** Cohere AI has developed a new method called model merging. Instead of combining data from different tasks and languages into one model, they propose merging separate models that are trained for specific tasks and languages. This allows each model to excel in its area before being combined, which improves safety and performance. **Advanced Merging Techniques** The merging process includes several techniques: - **Spherical Linear Interpolation (SLERP)**: This blends model strengths smoothly. - **Task Interference Elimination Strategy (TIES)**: This resolves conflicts between models to improve performance. - Other methods like linear merging and DARE-TIES also enhance the final model's effectiveness. **Proven Results** Research has shown significant improvements: - SLERP merging improved overall performance by 7% and reduced harmful outputs by 3.1%. - TIES merging reduced harmful outputs by 10.4%, though it slightly lowered general performance by 7.4%. - Language-specific merging led to a 6.6% reduction in harmful outputs and a 3.8% improvement in benchmarks. **Impact Across Languages** The improvements varied by language. For instance, Russian saw a 15% reduction in harmful outputs with TIES merging, while Spanish had a 10% performance boost. However, English models showed a decline in safety performance, emphasizing the need for tailored training. **A Comprehensive Framework for Safer AI** This research provides a strong framework for creating safer and more effective multilingual LLMs. By merging specialized models, the approach reduces the need for large amounts of training data and aligns safety standards across languages. **Conclusion: A Step Forward in AI Safety** Model merging is a significant advancement in balancing performance and safety in LLMs, especially in multilingual contexts. This method helps LLMs produce safe and high-quality outputs, particularly for languages with fewer resources. As AI evolves, techniques like model merging will be essential for building robust and safe AI systems across different languages and cultures. **Transform Your Business with AI** Learn how AI can improve your business: - **Identify Automation Opportunities**: Find areas where AI can help with customer interactions. - **Define KPIs**: Set clear goals to measure the impact of your AI efforts. - **Select an AI Solution**: Choose tools that meet your needs and allow customization. - **Implement Gradually**: Start small, collect data, and expand as needed. For advice on managing AI KPIs, contact us at hello@itinai.com. For more insights, follow us on Telegram or Twitter. Discover how AI can transform your sales and customer engagement at itinai.com.