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Analyzing Language Model Probability Assignments
A well-trained language model is tasked with calculating the probability of two sequences. It assigns a significantly higher probability to Sequence A than to Sequence B. Based on your understanding of how a model calculates sequence probability by breaking it down into a series of conditional probabilities, identify the single conditional probability term in Sequence B that is most responsible for its overall low probability, and explain your reasoning.
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Data Science
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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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