View and submit machine learning model evaluations
Find and download models from Hugging Face
Evaluate open LLMs in the languages of LATAM and Spain.
Open Persian LLM Leaderboard
Submit deepfake detection models for evaluation
Evaluate adversarial robustness using generative models
View NSQL Scores for Models
Teach, test, evaluate language models with MTEB Arena
Browse and filter machine learning models by category and modality
Evaluate code generation with diverse feedback types
Download a TriplaneGaussian model checkpoint
Compare and rank LLMs using benchmark scores
Explore GenAI model efficiency on ML.ENERGY leaderboard
The LLM Safety Leaderboard is a tool designed to benchmark and compare the safety performance of large language models (LLMs). It provides a platform to evaluate and rank models based on their adherence to safety guidelines, ethical considerations, and ability to generate responsible outputs. This leaderboard is essential for developers, researchers, and users to identify models that align with safety standards and mitigate potential risks associated with AI-generated content.
• Comprehensive Benchmarking: Evaluates LLMs across multiple safety dimensions, including bias reduction, misinformation avoidance, and ethical compliance.
• Transparent Scoring: Provides detailed scores and rankings based on standardized evaluation criteria.
• Comparison Tools: Allows side-by-side analysis of different models to identify strengths and weaknesses.
• User Submissions: Enables users to submit their own evaluations and contribute to the leaderboard.
• Regular Updates: Incorporates the latest models and evaluation metrics to stay current with industry advancements.
• Open-Access Data: Offers publicly available data for researchers and developers to improve model safety.
What is the purpose of the LLM Safety Leaderboard?
The purpose is to provide a standardized way to evaluate and compare the safety performance of LLMs, helping users make informed decisions about model usage.
How are models evaluated on the leaderboard?
Models are evaluated based on predefined safety metrics, including bias reduction, misinformation avoidance, and ethical compliance. These evaluations are conducted using a combination of automated testing and expert reviewing.
Can I submit my own model for evaluation?
Yes, the leaderboard allows users to submit their own models for evaluation, provided they meet the submission criteria. Visit the platform for detailed guidelines on how to contribute.