Understanding AI Language Models
Language models are sophisticated AI systems designed to interpret and generate human language by predicting subsequent words based on context. Large Language Models (LLMs) leverage extensive training data to perform a wide array of language tasks, including text generation and summarization. Despite their capabilities, these models exhibit limitations such as the potential for inaccuracies and a lack of real-time understanding.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
-
2.7.1Parameter Description
What we have learnt
- A language model is an AI system that predicts the next word in a sequence.
- Large Language Models like GPT are trained on vast datasets through processes like tokenization and reinforcement learning.
- LLMs possess strengths such as generating coherent text but also face limitations, including the risk of fabricating facts.
Key Concepts
- -- Language Model
- An AI system trained to understand and generate human language by predicting the next word in a sequence.
- -- Large Language Model (LLM)
- Advanced models with billions of parameters capable of performing a variety of language-related tasks.
- -- Tokenization
- The process of breaking down text into smaller pieces (tokens) for model training.
- -- Reinforcement Learning from Human Feedback (RLHF)
- A training methodology that utilizes human feedback to improve the model's accuracy and safety.
- -- Temperature and Topp Sampling
- Sampling strategies used to control the randomness and variety of model outputs.
Additional Learning Materials
Supplementary resources to enhance your learning experience.