Introduction

The years 2024 and 2025 marked significant advancements in the training process of large language models (LLMs). It was a period of exceptional innovation and progression. The growing interest in models, like ChatGPT which amassed over 200 million monthly users, spurred the development in LLM training.

Training Trends

One prime focus of LLM training in these years was the integration of real-time data for fact-checking. This revolutionary trend led to models accessing external sources and providing citations for their responses, thus generating more precise and up-to-date information than before. Implementing this innovation, models such as Microsoft Copilot enhanced their learning capacities by ingesting live internet data.

Another emerging trend that gained prominence was the creation of synthetic training data. It was an exciting development where models generated their own datasets. Google's LLM dramatically exhibited this improvement. It had the ability to develop questions and self-fine-tune based on the responses, significantly enhancing its performance.

Highlighting LLM Models

ChatGPT and Google’s LLMs stand out as the benchmark for trends and advancements in LLM training. These models integrated innovative techniques like real-time data ingestion and synthetic data generation, paving the path for future LLM training.

Conclusion

In retrospect, the years 2024 and 2025 were transformative for LLM training. The shift towards real-time data integration and synthetic data creation indicates an exciting future for LLM training. It will indeed be intriguing to see how these trends evolve and shape the future of LLMs.