7 LLM Generation Parameters—What They Do and How to Tune Them?

Tuning LLM outputs is essentially a decoding drawback: you form the mannequin’s next-token distribution with a handful of sampling controls—max tokens (caps response size beneath the mannequin’s context restrict), temperature (logit scaling for extra/much less randomness), top-p/nucleus and top-k (truncate the candidate set by chance mass or rank), frequency and presence penalties (discourage repetition or encourage novelty), and cease sequences (exhausting termination on delimiters). These seven parameters work together: temperature widens the tail that top-p/top-k then crop; penalties mitigate degeneration throughout lengthy generations; cease plus max tokens supplies deterministic bounds. The sections beneath outline every parameter exactly and summarize vendor-documented ranges and behaviors grounded within the decoding literature.

1) Max tokens (a.okay.a. max_tokens, max_output_tokens, max_new_tokens)

What it’s: A tough higher certain on what number of tokens the mannequin might generate on this response. It doesn’t increase the context window; the sum of enter tokens and output tokens should nonetheless match inside the mannequin’s context size. If the restrict hits first, the API marks the response “incomplete/size.”

When to tune:

• Constrain latency and price (tokens ≈ time and $$).
• Prevent overruns previous a delimiter once you can’t rely solely on cease.

2) Temperature (temperature)

What it’s: A scalar utilized to logits earlier than softmax:

Lower T sharpens the distribution (extra deterministic); increased T flattens it (extra random). Typical public APIs expose a variety close to [0,2][0, 2][0,2]. Use low T for analytical duties and increased T for inventive growth.

3) Nucleus sampling (top_p)

What it’s: Sample solely from the smallest set of tokens whose cumulative chance mass ≥ p. This truncates the lengthy low-probability tail that drives traditional “degeneration” (rambling, repetition). Introduced as nucleus sampling by Holtzman et al. (2019).

Practical notes:

• Common operational band for open-ended textual content is top_p ≈ 0.9–0.95 (Hugging Face steering).
• Anthropic advises tuning both temperature or top_p, not each, to keep away from coupled randomness.

4) Top-k sampling (top_k)

What it’s: At every step, limit candidates to the okay highest-probability tokens, renormalize, then pattern. Earlier work (Fan, Lewis, Dauphin, 2018) used this to enhance novelty vs. beam search. In fashionable toolchains it’s usually mixed with temperature or nucleus sampling.

Practical notes:

• Typical top_k ranges are small (≈5–50) for balanced range; HF docs present this as “pro-tip” steering.
• With each top_k and top_p set, many libraries apply k-filtering then p-filtering (implementation element, however helpful to know).

5) Frequency penalty (frequency_penalty)

What it’s: Decreases the chance of tokens proportionally to how usually they already appeared within the generated context, decreasing verbatim repetition. Azure/OpenAI reference specifies the range −2.0 to +2.0 and defines the impact exactly. Positive values scale back repetition; damaging values encourage it.

When to use: Long generations the place the mannequin loops or echoes phrasing (e.g., bullet lists, poetry, code feedback).

6) Presence penalty (presence_penalty)

What it’s: Penalizes tokens which have appeared no less than as soon as to date, encouraging the mannequin to introduce new tokens/matters. Same documented vary −2.0 to +2.0 in Azure/OpenAI reference. Positive values push towards novelty; damaging values condense round seen matters.

Tuning heuristic: Start at 0; nudge presence_penalty upward if the mannequin stays too “on-rails” and gained’t discover alternate options.

7) Stop sequences (cease, stop_sequences)

What it’s: Strings that power the decoder to halt precisely once they seem, with out emitting the cease textual content. Useful for bounding structured outputs (e.g., finish of JSON object or part). Many APIs enable a number of cease strings.

Design suggestions: Pick unambiguous delimiters unlikely to happen in regular textual content (e.g., "<|finish|>", "nn###"), and pair with max_tokens as a belt-and-suspenders management.

Interactions that matter

• Temperature vs. Nucleus/Top-k: Raising temperature expands chance mass into the tail; top_p/top_k then crop that tail. Many suppliers advocate adjusting one randomness management at a time to preserve the search house interpretable.
• Degeneration management: Empirically, nucleus sampling alleviates repetition and blandness by truncating unreliable tails; mix with gentle frequency penalty for lengthy outputs.
• Latency/price: max_tokens is probably the most direct lever; streaming the response doesn’t change price however improves perceived latency. (
• Model variations: Some “reasoning” endpoints limit or ignore these knobs (temperature, penalties, and many others.). Check model-specific docs earlier than porting configs.

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References:

• https://arxiv.org/abs/1904.09751
• https://openreview.web/discussion board?id=rygGQyrFvH
• https://huggingface.co/docs/transformers/en/generation_strategies
• https://huggingface.co/docs/transformers/en/main_classes/text_generation
• https://arxiv.org/abs/1805.04833
• https://aclanthology.org/P18-1082.pdf
• https://assist.openai.com/en/articles/5072263-how-do-i-use-stop-sequences
• https://platform.openai.com/docs/api-reference/introduction
• https://docs.aws.amazon.com/bedrock/newest/userguide/model-parameters-anthropic-claude-messages-request-response.html
• https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters
• https://cloud.google.com/vertex-ai/generative-ai/docs/be taught/prompts/adjust-parameter-values
• https://be taught.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning

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