Prompt strategies for model evaluation docs

When developing prompt strategies for model evaluation, there are several key aspects to consider in order to effectively assess model performance. Below are some strategies for crafting prompts that can be used in model evaluation documentation:

1. Clear Task Definition

• Strategy: Ensure the task is clearly defined and framed within the prompt. This avoids ambiguity and ensures the model’s output is relevant to the desired outcome.

• Example: If you want to evaluate the model’s ability to summarize, clearly define the task, e.g., “Summarize the following article in no more than three sentences.”

2. Diverse Data Types

• Strategy: Use varied data sources and formats (text, images, code, tables, etc.) in your prompts to evaluate the model’s versatility and adaptability.

• Example: “Given this image of a forest, describe the weather conditions likely present.”

3. Incremental Difficulty

• Strategy: Start with simple prompts and progressively increase the difficulty. This can help identify model weaknesses and areas where it might struggle.

• Example: Begin with basic questions such as “What is 2+2?” and move to more complex questions like “Explain the theory of relativity in simple terms.”

4. Open-Ended Prompts

• Strategy: Use open-ended prompts to test the model’s creativity and reasoning abilities. This is especially useful when evaluating generative models.

• Example: “Describe the future of artificial intelligence and its impact on society.”

5. Edge Case Scenarios

• Strategy: Craft prompts that cover edge cases or less common scenarios to ensure the model can handle rare or unexpected inputs.

• Example: “If a person is allergic to bees and a bee stings them, what should be the immediate course of action?”

6. Contextual Understanding

• Strategy: Test how well the model retains context over longer exchanges or complex narratives.

• Example: In a multi-turn conversation, ask the model to summarize key points or recall previous statements, like “What was the second point I mentioned about climate change?”

7. Bias and Fairness Testing

• Strategy: Design prompts that test for biases, whether related to race, gender, ethnicity, or other demographic factors. This can help identify any skewed outputs and refine the model for better fairness.

• Example: “Explain why a person might be more likely to succeed in a corporate setting.”

8. Grammatical and Stylistic Evaluation

• Strategy: Include prompts that test the model’s ability to produce grammatically correct and stylistically appropriate outputs.

• Example: “Write a formal email requesting vacation time” or “Write a casual tweet about a recent vacation.”

9. Time Sensitivity

• Strategy: Test the model’s ability to understand or process time-sensitive information.

• Example: “What are the most relevant global news events as of today, and why are they significant?”

10. Consistency Across Multiple Prompts

• Strategy: Ask the same question or request the same task multiple times using different wording to evaluate the consistency of the model’s responses.

• Example: “What is the capital of France?” followed by “Which city serves as the capital of France?”

11. Error Handling

• Strategy: Provide prompts that are likely to generate errors and see how the model responds. This can help in assessing robustness and reliability.

• Example: “What is the square root of ‘apple’?” or “Can you list the colors of the rainbow backwards?”

12. Precision and Accuracy

• Strategy: Craft prompts that require the model to provide precise and factual answers. Evaluate the model’s ability to deliver accurate information, especially for knowledge-based queries.

• Example: “What is the boiling point of water at sea level?”

13. Human-like Interaction

• Strategy: Assess the model’s ability to mimic natural human conversation, particularly in terms of empathy, humor, or cultural understanding.

• Example: “Tell me a joke about technology” or “How would you console a friend who is feeling sad?”

14. Summarization and Paraphrasing

• Strategy: Test how well the model can condense and rephrase information without losing meaning or introducing errors.

• Example: “Summarize the following paragraph in two sentences” or “Paraphrase the following sentence while retaining its original meaning.”

15. Semantic Understanding

• Strategy: Test the model’s ability to grasp the meaning behind a query, especially when it involves implied knowledge or context.

• Example: “If a person is carrying a red balloon, what might be a reason for their happiness?”

By creating a range of prompts that test these various aspects, you can assess a model’s strengths and weaknesses across different evaluation criteria, helping refine the model’s performance for your intended use cases.