LLM Inference Sampling Methods

Sampling methods in large language models are essential for fine-tuning the balance between accuracy and diversity in generated responses. Here’s a deeper dive into various sampling techniques—temperature sampling, top-K, top-P (nucleus sampling), min-P, and beam search—along with guidance on when to apply each.

1. Temperature Sampling

Temperature adjusts the level of randomness in the model's output. It scales the logits (the raw predictions for each possible next token) before they are converted into probabilities. Lower temperatures make the model more conservative, choosing higher-probability tokens, while higher temperatures introduce more diversity, choosing less-likely tokens more frequently.

• Low temperature (0.0 to 0.5): Good for factual, deterministic answers where you want minimal randomness. Lower temperatures lead to more predictable, often repetitive responses.
• Example use case: Math problems, precise Q&A, or coding, where you want the model to stick closely to high-confidence responses.
• Moderate temperature (0.7 to 1.0): Suitable for open-ended tasks requiring some creativity or variety. Moderate temperatures allow the model to explore plausible alternative tokens without drifting too far off topic.
• Example use case: Story generation, casual conversation, or brainstorming, where the response benefits from controlled variety.
• High temperature (1.0 and above): Use sparingly, mainly in creative or exploratory tasks, as it introduces significant randomness. High temperatures can yield creative or unexpected outputs but are prone to nonsensical or erratic responses.
• Example use case: Poetry, creative writing, or when you need high diversity in responses to gather a broad range of ideas.

2. Top-K Sampling

Top-K sampling limits the choices to the K most probable tokens for the next word, then samples from this restricted set. This method helps filter out unlikely options, making responses more coherent while still allowing for some variability.

• Low K (e.g., K=5 to 10): Provides highly focused responses by restricting the options significantly. This setting is helpful for tasks where you want slight variability but don’t want the answer to drift far from the main point.
• Example use case: Question-answering tasks where you want concise answers with minimal deviation, or formal writing where language should be precise.
• Moderate K (e.g., K=20 to 50): Gives more flexibility, maintaining coherence while allowing the model to consider a broader range of words. It’s a balanced setting that works for many general-purpose applications.
• Example use case: Dialogue generation, content summarization, and tasks that require flexibility but still need coherent language flow.
• High K (e.g., K=100 or more): Less restrictive, allowing for more creativity, but it can risk coherence if the topic is complex or structured. High values of K are rarely used as they introduce too much noise.
• Example use case: Creative storytelling where you need unique phrasing and can accept slight unpredictability.

3. Top-P (Nucleus Sampling)

Top-P (or nucleus sampling) considers only the smallest set of tokens whose cumulative probability exceeds a certain threshold, P (e.g., 0.9 or 0.95). This allows for dynamic sampling: instead of a fixed K number of options, it adapts to the probability distribution of tokens at each step.

• Low P (e.g., P=0.8 to 0.9): Restricts sampling to highly probable tokens, making responses focused and high-confidence. Suitable for structured and factual responses.
• Example use case: Technical writing, educational explanations, or scenarios where reliability is prioritized.
• Moderate P (e.g., P=0.9 to 0.95): Offers a balance of diversity and reliability, letting the model choose from a range of plausible tokens while excluding improbable ones. This is a sweet spot for many general-purpose applications.
• Example use case: Customer support dialogue or interactive storytelling, where responses should sound natural but not deviate too much.
• High P (e.g., P=0.95 to 0.99): Allows for more token variety, useful in scenarios where broader exploration of ideas is desirable. High values of P can result in creative but coherent responses.
• Example use case: Creative tasks like brainstorming or opinionated responses where you want a wide range of language without strict coherence.

4. Min-P Sampling

Min-P imposes a minimum probability threshold, only sampling tokens above a certain likelihood. This method helps avoid outlier or low-probability tokens that could disrupt coherence.

• Low min-P (e.g., Min-P=0.1): Effective for reducing the likelihood of low-probability, disruptive tokens without overly constraining diversity. This is useful in applications needing robust but flexible answers.
• Example use case: FAQ responses, where you want answers that vary but need to stay highly relevant and informative.
• High min-P (e.g., Min-P=0.3 to 0.5): Strictly limits token choice to high-probability options, enforcing conservative and concise responses. This setting is ideal for short, factual replies.
• Example use case: Formal and instructional content, where consistency and accuracy are paramount, such as legal or medical summaries.

5. Beam Search

Beam Search expands on the next token prediction by generating and evaluating several response paths (beams) in parallel, selecting the most coherent or likely complete sequence rather than focusing on individual next tokens. While it can be computationally expensive, it’s highly effective for ensuring structured and relevant responses.

• Short beam width (e.g., 3 beams): Maintains focus with minimal added computational cost. Small beam widths are appropriate for tasks where slight variability suffices, and efficiency is essential.
• Example use case: Customer support or technical assistance, where concise and accurate responses are required.
• Moderate to wide beam width (e.g., 5 to 10 beams): Widens the exploration scope, balancing coherence with some diversity. This is effective for tasks with longer answers where structure matters.
• Example use case: Summarization or paraphrasing, where structure and flow are essential, and the model needs flexibility to find the best wording.
• Wide beam width (10 or more beams): Maximizes exploration, useful for complex or highly structured responses but often costly and time-intensive.
• Example use case: Legal or technical document generation, where coherence and precision are critical, and computation resources can support the cost.

Choosing the Right Sampling Method for Your Task

Precision and Structure (e.g., Math, Technical Writing):

• Temperature: 0.0 to 0.3
• Top-P: 0.8 to 0.9
• Beam Search: Short to moderate beams (3 to 5)
• These settings help maintain coherence, avoid randomness, and ensure the response adheres to factual accuracy.

Balanced Responses with Flexibility (e.g., Q&A, Dialogue):

• Temperature: 0.5 to 0.7
• Top-P: 0.9 to 0.95
• Top-K: 20 to 50
• Moderate settings allow for natural variability, making responses sound more conversational while staying on topic.

Creativity and Exploration (e.g., Storytelling, Brainstorming):

• Temperature: 0.8 to 1.0+
• Top-P: 0.95 to 0.99
• Top-K: 50 to 100+
• High temperature, high P, or large K allow the model to explore diverse ideas, making responses more creative, but might risk coherence.

Diverse but Relevant Responses (e.g., Opinionated, Subjective Answers):

• Min-P: 0.1 to 0.3
• Best of N: Choosing the best output from multiple samples improves quality in subjective or opinion-based contexts.
• Using Min-P or Best-of sampling can help the model explore without straying into irrelevant answers.

When to use what - Summary Table