LLM (Large Language Model) testing refers to the process of evaluating and validating the performance, accuracy, safety, and reliability of large language models like GPT, LLaMA, or Claude. It involves various methodologies to ensure that the model functions as expected across different use cases.
Types of LLM Testing
- Functional Testing – Ensures the model correctly understands and generates text according to the given prompt.
- Bias & Fairness Testing – Checks for ethical issues, biases, and unintended discrimination in responses.
- Security Testing – Identifies vulnerabilities such as prompt injection attacks or adversarial exploits.
- Performance Testing – Evaluates speed, scalability, latency, and efficiency.
- Robustness Testing – Measures how well the model handles edge cases, ambiguous inputs, or adversarial prompts.
- Compliance Testing – Ensures adherence to legal, ethical, and industry standards (e.g., GDPR, HIPAA).
- User Experience Testing – Assesses how well the model aligns with user expectations and usability requirements.
- Hallucination Testing – Detects and quantifies misinformation or fabricated responses.
- Adversarial Testing – Subjects the model to hostile prompts to see if it can be manipulated.
Learn More in this blog:
https://www.confident-ai.com/blog/llm-testing-in-2024-top-methods-and-strategies#hallucination-testing