An autoregressive language model must calculate the total log-probability for the generated sentence: "The sky is blue." The process involves summing the conditional log-probabilities of each token in sequence. Below are four potential breakdowns of this calculation. Which one correctly represents the sequence of calculations the model must perform?
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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An autoregressive language model is generating a sequence and must choose between two possible next phrases to complete the input 'The mountain path was...'. The model has calculated the conditional log-probabilities for the tokens in each potential phrase as follows:
Phrase A: 'steep and rocky'
log Pr('steep' | 'The mountain path was') = -0.9log Pr('and' | 'The mountain path was steep') = -1.5log Pr('rocky' | 'The mountain path was steep and') = -0.7
Phrase B: 'long but scenic'
log Pr('long' | 'The mountain path was') = -1.2log Pr('but' | 'The mountain path was long') = -1.3log Pr('scenic' | 'The mountain path was long but') = -0.4
Based on these values, which phrase is the model more likely to generate, and what is its total log-probability?
Analyzing Suboptimal Autoregressive Generation
An autoregressive language model must calculate the total log-probability for the generated sentence: "The sky is blue." The process involves summing the conditional log-probabilities of each token in sequence. Below are four potential breakdowns of this calculation. Which one correctly represents the sequence of calculations the model must perform?