Types of Language Models

Evaluating language models

Shannon's Foundational Work on Language Modeling

Generalization of the Language Modeling Concept

Chain Rule for Sequence Probability

Deep Learning Approach to Language Modeling

Output Token Sequence in LLMs

Start of Sentence (SOS) Token

[CLS] Token as a Start Symbol

A system is designed to predict the probability of a sequence of words. For the sequence 'The dog ran', the system provides the following conditional probabilities:

• The probability of 'The' occurring at the start of a sequence is 0.2.
• The probability of 'dog' occurring after 'The' is 0.3.
• The probability of 'ran' occurring after 'The dog' is 0.7.

Based on the fundamental principle used by such systems to determine the likelihood of a full sequence, what is the overall probability of the sequence 'The dog ran'?

Analyzing Language Model Probability Assignments

A system's primary goal is to predict the probability of a sequence of tokens. To calculate the total probability for the sequence 'The quick brown fox', it breaks the problem down into a series of conditional probability calculations. Arrange the following calculations in the correct order that the system would use to find the total probability of the sequence.

Evaluating a Language Model's Probabilistic Output

Character-Level Language Model