Evaluating the Log-Likelihood Maximization Objective
A language model is fine-tuned on a large dataset of internet comments with the objective of maximizing the conditional log-likelihood of the data. While this approach makes the model very good at predicting the next word in a sequence similar to those in the training set, discuss at least one potential strength and two potential weaknesses of relying solely on this objective for creating a helpful and reliable AI assistant.
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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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Evaluation in Bloom's Taxonomy
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Auto-regressive Decomposition of Conditional Log-Likelihood
A machine learning engineer is fine-tuning two language models, Model A and Model B, on the same dataset of 100 prompt-response pairs. The goal is to select the model whose parameters are best optimized to make the observed responses most probable given the prompts. After one epoch of training, the engineer calculates the sum of the conditional log-probabilities for the entire dataset for each model:
- Model A: -150
- Model B: -200
Which model is performing better according to this objective, and why?
Evaluating the Log-Likelihood Maximization Objective
If a language model's parameters are perfectly optimized by maximizing the total conditional log-likelihood on a given training dataset, it means the model will assign a probability of 1.0 to every correct response sequence in that dataset.