Evaluating Candidate Sequences in LLM Inference
An LLM is generating a completion for the prompt: 'To improve team collaboration, we should...'. The inference process has produced three candidate sequences, and the model has calculated the total conditional log-probability for each one. Based on the objective of finding the most probable output sequence, which of the following options should be selected? Justify your choice.
0
1
Tags
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
Social Science
Empirical Science
Science
Related
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