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Kaz LLM Leaderboard is a data visualization tool designed to evaluate and compare the performance of Large Language Models (LLMs) using Kazakh multiple-choice tasks. It provides a comprehensive platform to assess the accuracy and effectiveness of different LLMs in understanding and responding to Kazakh language prompts. This leaderboard enables researchers and developers to identify top-performing models and gain insights into their strengths and weaknesses.
• Leaderboard Rankings: Displays the performance of various LLMs based on their accuracy in Kazakh multiple-choice tasks. • Filtering Options: Allows users to filter models by specific criteria, such as model size or training data. • Customizable Thresholds: Users can set accuracy thresholds to focus on high-performing models. • Interactive Visualizations: Presents data in an intuitive format, making it easy to compare performance metrics. • Model Comparison: Enables side-by-side comparison of multiple models to highlight differences. • Export Results: Users can download the results for further analysis. • Task Library: Access a repository of Kazakh language tasks for testing LLMs.
1. Why is Kaz LLM Leaderboard focused on Kazakh language tasks?
Kazakh language tasks are used to evaluate LLMs because they provide a unique perspective on how well models understand and process less-resourced languages. This helps in identifying models that excel in diverse linguistic contexts.
2. How is the accuracy of LLMs calculated on the leaderboard?
Accuracy is calculated based on the number of correct answers each model provides for the Kazakh multiple-choice tasks. The results are then normalized and presented in a comparative format.
3. Can I compare multiple models simultaneously?
Yes, the Kaz LLM Leaderboard allows users to select and compare multiple models side-by-side, making it easier to identify the best-performing models for specific tasks.