Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy

LLM Parameters

LLM Parameters refer to the elements within Large Language Models (LLMs) that dictate the model's behavior and language processing abilities.

LLM Parameters in practice

  1. Learning: During training, parameters adjust to predict words based on prior context.
  2. Mapping Relationships: Collectively, parameters form relationships between words and concepts in the training data.
  3. Temperature Regulation: A special parameter that influences the randomness of model outputs.
  4. Contribution to Architecture: They form the foundation of an LLM's ability to understand and generate language.
  5. Benchmark Setting: Adjustments to parameters are evaluated against set performance benchmarks.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.