KnowledgeBot - Whats the difference between training and using an Large Language Model (LLM)
Understanding Training, Testing, and Using a Large Language Model (LLM)
As AI technology advances, we’re seeing more and more applications of large language models (LLMs). But have you ever wondered how these LLMs work? In this post, we’ll break down the difference between training, using an LLM, and some key concepts.
Training an LLM
When a large language model is first trained, it’s like teaching a child to read. The teacher (the person or algorithm responsible for training) feeds the child a vast amount of text data, showing them how words fit together, how sentences structure, and what makes sense in different contexts.
Training: This process adjusts weights and biases within the model’s architecture, allowing it to learn from the data. Think of it like fine-tuning a bicycle – the LLM gets better at navigating language landscapes as more training occurs!
Using an LLM
When we use an LLM, we’re interacting with the learned patterns and relationships within the model. This can involve generating text, answering questions, or completing tasks like writing reports.
The good news is that using a trained LLM doesn’t require retraining it from scratch, unlike training! Since the model has already learned from its initial data, it can make predictions or generate output based on new input without needing to relearn everything. This makes inference (using an LLM) much safer and more confidential than training.
Why Inference is Safe and Confidential
Inference involves using a trained model to make predictions or generate output based on new input, without retraining the model from scratch. This means that:
- The model doesn’t need to access raw data or learn new patterns, reducing the risk of exposing sensitive information.
- The model can continue to perform tasks without requiring significant computational resources or updates, making it more energy-efficient and cost-effective.
In contrast, training involves feeding a large amount of data into an LLM, which can be time-consuming and resource-intensive. By using inference instead, organizations can maintain confidentiality and security while still benefiting from the capabilities of their trained models.
Key Concepts
- Inference: The process of using a trained model to make predictions or generate output based on new input.
- Training: Adjusting weights and biases in an LLM’s architecture using its original training data.
- RAG (Retrieval Augmented Generation): A type of large language model that uses retrieval algorithms to quickly search and retrieve relevant information from its training data, followed by generation to produce output.
RAG Advantages
- Improved adaptability to changing language landscapes
- Real-time context generation using pre-trained language models
By understanding these concepts, you’ll be better equipped to harness the power of LLMs for a wide range of applications while maintaining safety and confidentiality!