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Analyzing Chatbot Behavior with Conditional Probability
A company uses a sophisticated chatbot (at training version t=10000) to handle customer service inquiries. An analyst observes the following behavior:
- When a user asks, 'How can I return my purchase?', the chatbot provides the standard 30-day return policy.
- When a different user asks, 'As a VIP member, how can I return my purchase?', the chatbot provides an extended 90-day return policy.
Using the components of the conditional probability expression Pr^t(y|c, z), analyze why the chatbot provides two different responses. In your analysis, clearly identify what y, c, and z represent in this scenario.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
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A predictive text model is being trained. At an early stage of training (state t=100), it is given the context c = 'The sky is' and an additional instruction z = 'use a common color'. The model calculates the probability of the next word y = 'blue' as Pr^100('blue' | c, z) = 0.2. After extensive training (state t=5000), the model re-evaluates the same inputs and finds the probability to be Pr^5000('blue' | c, z) = 0.8. What is the most accurate interpretation of this change?
A language model is being prompted to generate a JSON object. The model is at training step 5000. The prompt is: 'Given the user's request to find a coffee shop, provide the output in a structured format.' The model is considering 'name' as the next part of the output. Match each component of the probability expression
Pr^t(y|c, z)to its corresponding element in this scenario.LLM Probability Distribution Notation ()
Analyzing Chatbot Behavior with Conditional Probability