A language model is generating text with the goal of producing the most probable output sequence. It has already generated the phrase 'The best way to learn is by...' and must now decide the next word. The model calculates the following probabilities for the next possible words:
Pr('doing' | 'The best way to learn is by...') = 0.6Pr('reading' | 'The best way to learn is by...') = 0.3Pr('sleeping' | 'The best way to learn is by...') = 0.09Pr('car' | 'The best way to learn is by...') = 0.01
To continue constructing the sequence with the highest possible overall probability, which word should the search process select at this step?
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
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A language model is generating text with the goal of producing the most probable output sequence. It has already generated the phrase 'The best way to learn is by...' and must now decide the next word. The model calculates the following probabilities for the next possible words:
Pr('doing' | 'The best way to learn is by...') = 0.6Pr('reading' | 'The best way to learn is by...') = 0.3Pr('sleeping' | 'The best way to learn is by...') = 0.09Pr('car' | 'The best way to learn is by...') = 0.01
To continue constructing the sequence with the highest possible overall probability, which word should the search process select at this step?
Limitations of Probability Maximization in Text Generation
Evaluating Candidate Sequences in LLM Inference