A Base da Inteligência Artificial Moderna
Narrator: The Training Process: Pre-training and Fine-tuning
LLMs are trained through a massive 'pre-training' phase, primarily by predicting the next word from vast text datasets. This is followed by 'fine-tuning,' where the model learns to follow instructions using smaller, specific datasets of prompt-response pairs, aligning its capabilities for practical use.
Narrator: Aligning LLMs with Human Feedback (RLHF)
Reinforcement Learning from Human Feedback (RLHF) refines LLMs by using human rankings of model outputs to train a reward model. This reward model then guides the LLM to generate responses that are not only useful but also safe and aligned with human preferences, moving beyond simple text completion.
Narrator: Limitations: Hallucinations and Context Window
LLMs can 'hallucinate' by confidently presenting fabricated information because they learn statistical patterns, not facts. They also have a finite 'context window,' a limit on how much past information they can process, causing them to 'forget' earlier parts of long conversations.
Narrator: Differentiating LLMs: Data, Size, and Alignment
While most LLMs share the Transformer base, differences arise from training data (influencing knowledge), parameter count (affecting capacity and cost), and alignment methods (defining personality via fine-tuning and RLHF). These factors explain why models like Cloud and GPT respond differently.
Narrator: The Rise of Open-Source LLMs
Open-source models like DeepSeek and Minimax offer transparency and accessibility, allowing study, modification, and commercial use. DeepSeek's R1 model, for instance, achieved strong performance in benchmarks and was trained at a significantly lower cost than proprietary counterparts, demonstrating the viability of open-source development.
