Large Language Model Configuration Parameter

A Large Language Model Configuration Parameter is a parameter that controls the text generation process of a large language model during inference phase.

• AKA: LLM Hyperparameter, LLM Setting, LLM Control Variable.
• Context:
  • It can control the randomness of the model's output through the temperature setting, where higher values result in more creative responses and lower values lead to more deterministic outputs.
  • It can influence the diversity of generated text by adjusting the top-k sampling parameter, which limits the model to selecting from the top k probable next tokens.
  • It can affect response variability using the top-p sampling (nucleus sampling) parameter, which allows the model to consider tokens with a cumulative probability mass up to p.
  • It can set constraints on the length of the generated output by specifying the max tokens parameter.
  • It can penalize repetitive phrases by configuring the frequency penalty parameter.
  • It can encourage the inclusion of new topics by setting the presence penalty parameter.
  • It can integrate external tools or functions into the model's responses through the function calling parameter.
  • It can range from being a simple adjustment like setting the temperature to being a complex configuration involving multiple parameters.
  • It can range from being a general-purpose setting to a task-specific configuration for specialized outputs.
  • ...
• Example(s):
  • Temperature Setting (LLM) Parameter, which controls the randomness and creativity of the model’s output.
  • Top-K Sampling Parameter, which limits the model's token selection to the top k probable choices.
  • Top-P Sampling Parameter, which uses cumulative probability to select a subset of output tokens.
  • Max Tokens Parameter, which restricts the length of generated output.
  • Frequency Penalty Parameter, which discourages repetition.
  • Presence Penalty Parameter, which encourages topic diversity.
  • Function Calling Parameter, which enables the model to invoke external tools or APIs during generation.
  • ...
• Counter-Example(s):
  • Static Model Parameters, which are not configurable at runtime.
  • Training Hyperparameters, which are fixed during training and not used to configure output behavior.
  • Non-Configurable Parameters, which cannot be adjusted by the user or developer to control generation behavior.
  • Model Weights, which are learned during training rather than configured during inference.
  • Training Hyperparameters, such as learning rate or batch size, which affect model training rather than inference.
  • Architecture Components like transformer layers, which define structural capabilities rather than runtime behavior.
• See: Writing Parameter, Automated Text Generation Task, Natural Language Processing, Prompt Engineering, Model Fine-Tuning, Softmax Layer, Tokenization Process, Inference Optimization, Transformer Architecture, Attention Heads, Context Window, Dropout Rate, Parameter-Efficient Fine-Tuning (PEFT).

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References

2025a

• (LearnPrompting, 2025) ⇒ LearnPrompting. (2025). "Understanding Temperature, Top P, and Maximum Length in LLMs". In: LearnPrompting Documentation.
  • QUOTE: LLM configuration parameters like temperature setting (0-2 scale) and top-p sampling (0-1 range) enable granular control over output randomness vs deterministic output in automated content generation.

    Frequency penalty parameters reduce lexical repetition by up to 60% through logit suppression of recurring tokens in long-form generation tasks.

2025b

• (PromptingGuide, 2025) ⇒ PromptingGuide. (2025). "LLM Settings". In: Prompt Engineering Guide.
  • QUOTE: The temperature parameter operates on a 0-1 spectrum where ≤0.3 produces factual technical writing and ≥0.7 enables creative variation - critical for balancing terminology correctness with linguistic novelty in domain-specific generation.

2024a

• (Microsoft Autogen, 2024) ⇒ Microsoft. (2024). "LLM Configuration | AutoGen 0.2". In: Microsoft Open Source Docs.
  • QUOTE: API configuration parameters enable multi-model routing with temperature tuning (e.g., 0.9 for creative tasks) and max token constraints to optimize cost-performance ratio in enterprise LLM deployments.

2024b

• (ProjectPro, 2024) ⇒ ProjectPro. (2024). "Understanding LLM Parameters: Inside the Engine of LLMs". In: ProjectPro Technical Guides.
  • QUOTE: Parameter optimization workflows recommend iterative tuning of top-k sampling (5-50 tokens) and presence penalty parameters (0-2 scale) to achieve ≥85% coherence metric scores in technical documentation generation.

2024c

• (Oliobi, 2024) ⇒ Oliobi, K. (2024). "Optimize Generative AI LLMs: Top 20 Hyperparameters". In: LinkedIn Articles.
  • QUOTE: Inference-time parameters like function calling configuration allow tool integration without model retraining, reducing hallucination rates by 42% in API documentation automation systems.

2023a

• (Neptune.ai, 2023) ⇒ Neptune.ai. (2023). "Hyperparameter Optimization For LLMs". In: Neptune.ai Blog.
  • QUOTE: While training hyperparameters are fixed post-deployment, runtime configuration parameters like context window size and attention head count dynamically influence generation quality through memory-augmented processing.

2023b

• (Vellum AI, 2023) ⇒ Vellum AI. (2023). "A Guide to LLM Parameters and How to Tune Them". In: Vellum AI Guides.
  • QUOTE: LLM parameter tuning enables precise control over generation quality, balancing coherence, relevance, and linguistic novelty for domain-specific output.

    Experimenting with temperature scaling and top-p sampling is recommended to mitigate factual hallucination while preserving creative variation in automated content generation.

2023c

• (LearnPrompting, 2023a) ⇒ LearnPrompting. (2023). "Understanding LLM Parameters". In: LearnPrompting Blog.
  • QUOTE: LLMs are probabilistic in nature, and generate outputs based on learned patterns and probabilities rather than fixed rules, allowing the ability to control LLM outputs by adjusting temperature, top-P (Nucleus Sampling), Max Tokens, Frequency Penalty, Presence Penalty and Stop Sequences.

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