A language model has calculated the following raw scores (logits) for the next potential token: {'mat': 3.0, 'rug': 2.5, 'chair': 2.0, 'moon': -1.0}. To control the randomness of the output, a temperature parameter is applied to these scores before they are converted into a final probability distribution for sampling. Which of the following probability distributions most likely resulted from applying a low temperature (e.g., a value less than 1.0)?
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Ch.5 Inference - 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
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Token Sampling from a Conditional Probability Distribution
Temperature-Scaled Softmax for Renormalized Probability
A language model has calculated the following raw scores (logits) for the next potential token:
{'mat': 3.0, 'rug': 2.5, 'chair': 2.0, 'moon': -1.0}. To control the randomness of the output, a temperature parameter is applied to these scores before they are converted into a final probability distribution for sampling. Which of the following probability distributions most likely resulted from applying a low temperature (e.g., a value less than 1.0)?Troubleshooting a Factual Chatbot's Output
You are configuring a text generation model for different tasks. Match each task with the description of the temperature setting that would be most appropriate to achieve the desired output.