This repository is dedicated to providing a comprehensive guide to testing Large Language Models (LLMs) like OpenAI's GPT series. It covers a range of testing methodologies designed to ensure that LLMs are reliable, safe, unbiased, and efficient across various applications. Each type of testing is crucial for developing LLMs that function effectively and ethically in real-world scenarios.
This guide includes the following categories of testing, each contained in its respective directory:
- Adversarial Testing: Techniques to challenge the model with tricky or misleading inputs to ensure robustness.
- Behavioral Testing: Ensures the model behaves as expected across a range of scenarios.
- Compliance Testing: Checks adherence to legal and ethical standards.
- Factual Correctness Testing: Verifies the accuracy of the information provided by the model.
- Fairness and Bias Testing: Assesses outputs to ensure they are free of demographic biases.
- Integration Testing: Evaluates how well the LLM integrates with other software systems.
- Interpretability and Explainability Testing: Tests the model’s ability to explain its decisions.
- Performance Testing: Measures the efficiency and scalability of the model under various loads.
- Regression Testing: Ensures new updates do not disrupt existing functionalities.
- Safety and Security Testing: Ensures the model does not suggest or enable harmful behaviors.
Each directory contains a detailed README.md that explains the specific testing methods used, along with examples.md providing practical examples and scenarios for conducting the tests.
To use this guide:
- Navigate to any testing category directory that aligns with your testing needs.
- Read the
README.mdfor an overview and detailed explanation of the testing focus in that category. - Explore the
examples.mdfor specific test scenarios, expected outcomes, and guidance on implementing the tests.