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Questions and Answers
What is the primary function of the decoder in a transformer model?
What type of language model is used for predicting the next word in a sentence?
What is the primary goal of an auto-encoding model?
What is the name of the family of models used for Natural Language Generation (NLG)?
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What is the primary goal of an auto-regressive model?
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Study Notes
Transformer Architecture
- A transformer consists of an encoder and a decoder
- The encoder takes in input and outputs a matrix representation of that input
- The decoder takes in that representation and iteratively generates an output
Language Modeling
- A language model is trained to predict a missing word in a sequence of words
- There are two types of language models: auto-regressive and auto-encoding
Auto-Regressive Models
- Goal: predict a future token (word) given either the past tokens or the future tokens but not both
- Applications:
- Predicting next word in a sentence (auto-complete)
- Natural Language Generation (NLG)
- GPT Family
Auto-Encoding Models
- Goal: learn representations of the entire sequence by predicting tokens given both the past and future tokens
- Applications:
- Comprehensive understanding and encoding of entire sequences of tokens
- Natural Language Understanding (NLU)
- BERT
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Description
Understand the core components of transformer models, including the encoder and decoder, and learn about the different types of language models used in natural language processing tasks. Test your knowledge of auto-regressive and auto-encoding models and how they're used to predict future tokens in a sequence of words.
