Temperature and sampling in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the role of temperature in sampling from a language model?

Temperature controls how random or focused the model's predictions are. A low temperature (<1) makes the model more confident and conservative, picking high-probability words. A high temperature (>1) makes the model more creative and random by flattening the probabilities.

beginner

Explain sampling in the context of generating text from a language model.

Sampling means picking the next word based on the model's predicted probabilities instead of always choosing the most likely word. This adds variety and creativity to the generated text.

intermediate

How does increasing temperature affect the probability distribution during sampling?

Increasing temperature makes the probability distribution more even, so less likely words have a higher chance to be picked. This leads to more diverse and surprising outputs.

intermediate

What happens if temperature is set to 0 during sampling?

Setting temperature to 0 means always picking the word with the highest probability (greedy decoding). This removes randomness and can make the output repetitive or dull.

beginner

Why might you want to use a moderate temperature (e.g., 0.7) instead of very low or very high?

A moderate temperature balances creativity and coherence. It allows some randomness for interesting text but keeps the output sensible and relevant.

What does a temperature of 1.0 mean when sampling from a language model?

A Probabilities are flattened to be more even

B The original predicted probabilities are used without change

C Only the highest probability word is chosen

D Probabilities are sharpened to favor the top word

A temperature of 1.0 means the model uses the original predicted probabilities as they are, without scaling.

What is the effect of setting temperature to a very high value (e.g., 5)?

A Model always picks the most likely word

B Output becomes very predictable and repetitive

C Model ignores probabilities and picks words randomly

D Output becomes more random and diverse

High temperature flattens the probability distribution, increasing randomness and diversity in output.

Which sampling method removes randomness completely?

A Greedy decoding (temperature = 0)

B Random sampling without temperature

C Sampling with temperature = 1

D Sampling with temperature > 1

Greedy decoding always picks the highest probability word, removing randomness.

Why is sampling preferred over always picking the highest probability word?

A It makes the output more creative and less repetitive

B It guarantees the most accurate output

C It speeds up the generation process

D It reduces the model size

Sampling adds randomness, making generated text more varied and interesting.

What does lowering temperature below 1 do to the output?

A Makes output more random

B Makes output longer

C Makes output more focused and conservative

D Makes output shorter

Lower temperature sharpens the probability distribution, making the model pick more likely words.

Describe how temperature affects the randomness of text generated by a language model.

Think about how temperature changes the chance of picking less likely words.

Explain why sampling is used instead of always choosing the most likely word in text generation.

Consider how always picking the top word might affect the text.