Analyzing Model Training with Flawed Data
A team is training a language model for a customer support chatbot. They are using a standard supervised learning approach where the model's goal is to maximize the probability of generating the exact responses found in their training dataset. The dataset consists of thousands of real chat logs between human agents and customers. However, the team discovers that in about 5% of these logs, the human agent provided a factually incorrect or unhelpful answer.
Analyze how the training objective will likely affect the model's responses when it is deployed and encounters questions similar to those that were answered incorrectly in the training data. Explain the underlying mechanism of the training objective that leads to this outcome.
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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
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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A language model is being trained with a supervised objective to maximize the probability of the correct output. Given the input 'The largest city in the US is', the target output is the two-token sequence 'New York'. Two different models are evaluated on this single instance.
- Model A predicts the first token 'New' with a probability of 0.6, and then predicts the second token 'York' with a probability of 0.8.
- Model B predicts the first token 'New' with a probability of 0.9, and then predicts the second token 'York' with a probability of 0.4.
Based on the standard training objective for this task, which statement correctly analyzes the models' performance on this specific example?
Analyzing Model Training with Flawed Data
Limitations of Supervised Fine-Tuning for LLM Alignment
Parameter Updates in Supervised LLM Training