Temperature and sampling in NLP - Model Pipeline Trace

Model Pipeline - Temperature and sampling

This pipeline shows how temperature and sampling affect text generation in language models. Temperature controls randomness, and sampling picks the next word based on probabilities.

Data Flow - 6 Stages

1Input Text

1 sentence (variable length)→User provides a starting sentence or prompt→1 sentence (variable length)

"The weather today is"

↓

2Tokenization

1 sentence (variable length)→Split sentence into tokens (words or subwords)→1 sequence x 4 tokens

["The", "weather", "today", "is"]

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3Model Prediction

1 sequence x 4 tokens→Model predicts next word probabilities→1 sequence x vocabulary size (e.g., 50,000)

{"sunny": 0.3, "rainy": 0.2, "cloudy": 0.1, ...}

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4Apply Temperature

1 sequence x vocabulary size→Adjust probabilities by temperature to control randomness→1 sequence x vocabulary size

Temperature=0.5 makes distribution sharper; Temperature=1.5 makes it flatter

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5Sampling

1 sequence x vocabulary size→Randomly pick next word based on adjusted probabilities→1 token

"sunny"

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6Output Text

1 token→Add chosen token to sentence→1 sentence (variable length + 1 token)

"The weather today is sunny"

Training Trace - Epoch by Epoch

Loss
2.5 |****
2.0 |***
1.5 |**
1.0 |*
0.5 |
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.5	0.30	Model starts learning word patterns with high loss and low accuracy
2	1.8	0.45	Loss decreases and accuracy improves as model learns better predictions
3	1.3	0.60	Model shows steady improvement in predicting next words
4	1.0	0.70	Loss continues to decrease; model becomes more confident
5	0.8	0.78	Training converges with good accuracy and low loss

Prediction Trace - 5 Layers

Layer 1: Tokenization

Layer 2: Model Prediction

Layer 3: Apply Temperature (T=0.5)

Layer 4: Sampling

Layer 5: Output Text

Model Quiz - 3 Questions

What does lowering the temperature value do to the word probabilities?

A Makes the distribution sharper, favoring high-probability words

B Makes the distribution flatter, increasing randomness

C Removes low-probability words completely

D Does not affect the probabilities

Key Insight

Temperature controls how creative or predictable the model's text is by adjusting word choice randomness. Sampling uses these adjusted probabilities to generate varied and interesting text outputs.