Learn Before
Example of 1-Best Selection
An example of the 1-best selection process begins with a set of five candidate words, which are then ranked by probability: 'cute' (Pr = 0.34), 'on' (Pr = 0.32), 'sick' (Pr = 0.21), 'are' (Pr = 0.12), and '.' (Pr = 0.01). In the final output stage, the word 'cute' is selected because it has the highest score, while all other candidates are pruned.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Example of 1-Best Selection
Ranking Stage in 1-Best Selection
Expansion Stage in 1-Best Selection
Output Stage in 1-Best Selection
Predicting the Next Word
A language model is determining the next word in a sequence. It follows a process where it first creates a list of possible words, then organizes them by likelihood, and finally chooses the most probable one. Arrange the formal stages of this process in the correct chronological order.
A language model is using a three-stage process (Expansion, Ranking, Output) to select the next word for the phrase 'The cat is...'. The model first expands the possibilities to a set of candidates with their probabilities: 'sleeping' (0.5), 'cute' (0.3), 'on' (0.15), and 'blue' (0.05). However, the model's final output is the word 'on'. Which stage of the process is the most direct point of failure?
Learn After
A language model is generating the next word in a sentence. After calculating the probabilities for several candidate words, it has the following set: 'the' (probability = 0.45), 'a' (probability = 0.25), 'my' (probability = 0.15), and 'your' (probability = 0.10). If the model uses a selection method where only the single most probable candidate is chosen and all others are discarded, which word will be selected for the final output?
Applying the 1-Best Selection Method
Inferring Probability from Model Output