A language model uses an ensemble of 3 prompts (K=3) to generate the next token in a sequence. The model selects the token ŷ_j that maximizes the sum of log-probabilities across all prompts, according to the formula: ŷ_j = argmax(y_j) Σ(k=1 to K) log Pr(y_j | ...).
Given the following log-probabilities for two candidate tokens, 'cat' and 'dog', which token will the model select?
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Application in Bloom's Taxonomy
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Applying Token-Level Averaging for Text Generation
In a text generation process that combines outputs from
Kdifferent prompts, the next tokenŷ_jis chosen according to the following decision rule:ŷ_j = argmax(y_j) Σ(k=1 to K) log Pr(y_j | x_k, ŷ_1, ..., ŷ_{j-1})What is the primary analytical reason for summing the log-probabilities (
log Pr) of a candidate token across all prompts, rather than multiplying the raw probabilities (Pr)?A language model uses an ensemble of 3 prompts (K=3) to generate the next token in a sequence. The model selects the token
ŷ_jthat maximizes the sum of log-probabilities across all prompts, according to the formula:ŷ_j = argmax(y_j) Σ(k=1 to K) log Pr(y_j | ...).Given the following log-probabilities for two candidate tokens, 'cat' and 'dog', which token will the model select?