Code as a Catalyst: Improving LLM Capabilities Across Diverse Tasks

Practical Solutions for Improving LLM Capabilities Large Language Models (LLMs) are being enhanced by including code data in their pre-training process. Research shows that incorporating code data led to significant improvements in natural language reasoning, world knowledge, generative win rates, and code performance. Key Findings Compared to text-only pre-training, including code data resulted in 8.2% increase in natural language reasoning, 4.2% in world knowledge, 6.6% in generative win rates, and a remarkable 12-fold boost in code performance. Additional improvements were observed when performing cooldown with code, resulting in 3.7% increase in natural language reasoning, 6.8% in world knowledge, and a 20% boost in code performance. Practical Insights Optimizing the proportion of code, enhancing code quality through synthetic code and code-adjacent data, and utilizing code across multiple training stages, including cool down, are crucial for improving LLM performance. Incorporating code data not only enhances reasoning capabilities but also improves the overall quality of generated content across various tasks. AI Solutions for Business Transformation To evolve your company with AI and stay competitive, consider using Code as a Catalyst: Improving LLM Capabilities Across Diverse Tasks to redefine your work processes. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to harness the power of AI for your business. Connect with Us For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Stay tuned on our Telegram or Twitter for the latest updates.