Practical Solutions for Improving LLM Capabilities
Understanding the Impact of Code Data on Large Language Models (LLMs)
Large Language Models (LLMs) have gained significant attention as researchers focus on enhancing their performance across various tasks. A critical challenge lies in understanding how pre-training data, particularly code data, influences their overall capabilities.
Researchers have conducted extensive experiments to investigate the impact of code data on LLM performance. Their findings reveal significant improvements in natural language reasoning, world knowledge, generative win rates, and code performance when code data is included in the pre-training process.
Key Findings
Compared to text-only pre-training, the inclusion of code data led to relative increases of 8.2% in natural language reasoning, 4.2% in world knowledge, 6.6% in generative win rates, and a remarkable 12-fold boost in code performance.
Furthermore, 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 factors for improving LLM performance.
Incorporating code data not only enhances reasoning capabilities but also improves the overall quality of generated content across various tasks, highlighting the broad benefits of code data in LLM training.
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