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Analyzing Generation Outcomes
A language model is about to generate the next token. The potential tokens and their initial probabilities are listed in the case study below. The model ultimately generates the token 'bird'. Based on this outcome, determine the possible range for the probability threshold 'p' that was used. Explain your reasoning by describing how the selection and pruning steps of the process would lead to this result.
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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
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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A text generation model uses a method where it selects the smallest set of most likely next words whose combined probability is at least a certain threshold, renormalizes their probabilities to sum to 1, and then samples from that new set. Given the following sorted list of potential next words and their initial probabilities, and a probability threshold of 0.7, which option correctly shows the set of words and their new probabilities after selection and renormalization, just before the final sampling step?
Initial Probabilities:
- "the": 0.40
- "a": 0.20
- "an": 0.15
- "one": 0.10
- "some": 0.08
A language model is configured to generate the next word in a sentence using a specific text generation technique. This technique involves identifying a core set of the most probable words based on a cumulative probability threshold, adjusting their probabilities, and then making a final selection. Arrange the following actions into the correct sequence as they would occur within this specific stage of the generation process.
Analyzing Generation Outcomes