A research team is fine-tuning a large language model using a combined loss objective, which includes both a standard language model (LM) loss against ground-truth data and a knowledge distillation (KD) loss from a weaker supervisor model. They observe that while the large model is very good at mimicking the style and general structure of the weak supervisor's outputs, it frequently makes factual errors that are not present in the ground-truth dataset. Which of the following is the most likely cause of this issue and the best corrective action?
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
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A team is fine-tuning a large language model. They have access to a small, high-quality dataset with verified ground-truth labels, as well as a much larger dataset where labels have been generated by a weaker, smaller model. To maximize the performance of the large model by using both data sources simultaneously, which training objective should they implement?
Visual Diagram of Combined Loss Training for Weak-to-Strong Generalization
Rationale for a Hybrid Training Objective
A research team is fine-tuning a large language model using a combined loss objective, which includes both a standard language model (LM) loss against ground-truth data and a knowledge distillation (KD) loss from a weaker supervisor model. They observe that while the large model is very good at mimicking the style and general structure of the weak supervisor's outputs, it frequently makes factual errors that are not present in the ground-truth dataset. Which of the following is the most likely cause of this issue and the best corrective action?