LLM Sampling Techniques: Minimum Probability and Temperature
Minimum Probability Sampling
Definition
Minimum probability sampling is a technique used in language model APIs to balance between diversity and coherence in the model’s output.
How it works
- Sets a dynamic threshold for token selection based on the probability of the most likely token.
- The threshold is a fraction (determined by the min_p value) of the top token’s probability.
Example explanation
Let’s say min_p = 0.1, and we’re generating the next token:
Scenario A:
- Most likely token probability: 95%
- Threshold: 95% * 0.1 = 9.5%
- Only tokens with probabilities ≥ 9.5% are considered
Scenario B:
- Most likely token probability: 10%
- Threshold: 10% * 0.1 = 1%
- Tokens with probabilities ≥ 1% are considered
Adaptive nature
- When the model is very confident (high top probability), the threshold is higher, limiting options to maintain coherence.
- When the model is less certain (lower top probability), the threshold lowers, allowing more diverse options.
Benefits
- Preserves diversity for open-ended choices
- Maintains coherence for deterministic choices (e.g., programming syntax)
- Allows higher temperatures without losing coherence
Temperature in LLM Sampling
Definition
Temperature controls the randomness in token selection during text generation.
Effects of Higher Temperature
- Increased diversity in outputs
- Exploration of less likely options
- Reduced repetitiveness
- Better performance on open-ended tasks
- Potential mitigation of model biases
- Improved resilience to prompt engineering
Challenges
- Maintaining coherence and relevance at higher temperatures
Optimal Use
- Lower temperatures: Tasks requiring high accuracy or factual correctness
- Higher temperatures: Creative or exploratory tasks
Synergy: min_p and Temperature
Combining min_p sampling with higher temperatures allows for:
- Increased creativity and diversity in outputs
- Maintained coherence by filtering out extremely improbable tokens
Key Takeaways
- min_p sampling adapts token selection threshold based on the model’s confidence.
- Higher temperatures increase output diversity but risk coherence.
- Combining min_p with higher temperatures balances creativity and coherence.
- The optimal sampling strategy depends on the specific task and desired outcome.