In the video, Andrej Karpathy explores the problem of hallucinations in Large Language Models (LLMs), defined as the generation of incorrect or fabricated information. These hallucinations arise because LLMs generate responses based on statistical patterns in their training data rather than factual knowledge like humans. The article highlights that while early models frequently exhibited these issues, advances in training methodologies have improved their accuracy. Understanding the training pipeline, which includes pretraining, supervised fine-tuning, and reinforcement learning, is crucial to comprehend these models' limitations and challenges.
The instances where LLMs generate incorrect, misleading, or entirely fabricated information that appears plausible are known as hallucinations.
LLMs do not know facts in the way humans do; instead, they predict words based on patterns in their training data.
Early models released a few years ago struggled significantly with hallucinations, but mitigation strategies have improved over time.
Training LLMs typically involves three major stages: Pretraining, Supervised Fine-Tuning, and Reinforcement Learning with Human Feedback.
Collection
[
|
...
]