Stick To Your Role! Leaderboard

What is Stick To Your Role! Leaderboard ?

Stick To Your Role! Leaderboard is a tool designed for comparing large language models (LLMs) by evaluating their role stability. It helps users understand how well different models adhere to their assigned roles and behaviors in various conversational and task-oriented scenarios. This leaderboard provides insights into model performance and consistency, enabling users to make informed decisions about which models best suit their needs.

Features

• Role Stability Metrics: Evaluates how consistently models maintain their assigned roles and behaviors. • Benchmark Comparisons: Compares multiple LLMs side-by-side based on their performance in role-specific tasks. • Data Visualization: Presents results in an intuitive leaderboard format for easy understanding. • Model Recommendations: Suggests models that excel in specific roles or scenarios. • Regular Updates: Incorporates the latest models and benchmarks to keep the evaluations current.

How to use Stick To Your Role! Leaderboard ?

1. Select Models: Choose the LLMs you want to compare from the available options.
2. Define Roles: Specify the roles or scenarios you want the models to adhere to.
3. Generate Metrics: Run the evaluation to compute role stability scores.
4. Analyze Results: Review the leaderboard to compare performance across models.
5. Use Recommendations: Leverage the tool's suggestions to identify the best model for your use case.

Frequently Asked Questions

What is role stability, and why is it important?
Role stability refers to how consistently a model maintains its assigned role or behavior during interactions. It is crucial for ensuring reliability and predictability in applications where specific roles are required.

How often are the models updated on the leaderboard?
The models on the leaderboard are updated regularly to include new releases and updates from leading AI providers, ensuring the most current comparisons.

Can I customize the roles or scenarios tested?
Yes, users can define specific roles or scenarios to evaluate how well models perform within their particular use cases.