Evaluating Llms Harness

What it does

This skill, "Evaluating LLMs Harness," provides a framework for systematically evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and run tests against different models, providing structured feedback. The harness facilitates comparing model performance across various benchmarks and tasks, enabling informed decisions about which LLM best suits specific needs.

When to use it

• Model Selection: Compare the performance of several LLMs on a defined set of tasks before choosing one for your application.
• Benchmark Tracking: Monitor the progress of an LLM over time as it's fine-tuned or updated.
• Custom Evaluation: Assess LLMs against custom evaluation criteria specific to your use case, beyond standard benchmarks.
• Performance Troubleshooting: Identify weaknesses in a particular LLM by running targeted evaluations.

Key capabilities

• Defines evaluation criteria.
• Runs tests against different LLMs.
• Provides structured feedback on model performance.
• Facilitates comparison of models across tasks.

Example prompts

• "Evaluate GPT-4 and Claude 3 Opus on summarizing news articles."
• "Run the 'creative writing' benchmark against Llama 3 70B."
• "Compare Gemini 1.5 Pro and Mistral Large on answering questions about historical events."

Tips & gotchas

The skill requires a clear definition of evaluation criteria for meaningful results. Ensure you have access to the LLMs you intend to evaluate, as this skill acts as an evaluator, not a provider of models themselves.