View and submit LLM evaluations
Merge machine learning models using a YAML configuration file
Evaluate and submit AI model results for Frugal AI Challenge
Submit models for evaluation and view leaderboard
View NSQL Scores for Models
Search for model performance across languages and benchmarks
Measure execution times of BERT models using WebGPU and WASM
Browse and evaluate ML tasks in MLIP Arena
Compare LLM performance across benchmarks
Browse and evaluate language models
Open Persian LLM Leaderboard
Compare audio representation models using benchmark results
Evaluate AI-generated results for accuracy
Hallucinations Leaderboard is a tool designed for evaluating and benchmarking large language models (LLMs). It provides a platform to view and submit evaluations of model performance, with a focus on understanding and mitigating hallucinations—instances where models produce inaccurate or non-factual information.
• Leaderboard System: Compare performance of different LLMs based on hallucination metrics. • Benchmarking Tools: Access standardized tests and evaluations for assessing model accuracy. • Customizable Metrics: Define and apply specific criteria for measuring hallucinations. • Model Comparison: Directly compare multiple models side-by-side. • Submission Interface: Easily submit your own evaluations for inclusion in the leaderboard. • Filtering and Sorting: Narrow down results by model size, architecture, or performance thresholds. • Real-Time Updates: Stay current with the latest evaluations and benchmarks.
What is the purpose of Hallucinations Leaderboard?
The purpose is to provide a centralized platform for evaluating and comparing LLMs, with a focus on reducing hallucinations and improving model accuracy.
How do I submit my own evaluations?
To submit evaluations, use the submission interface on the platform. Ensure your results align with the defined metrics and criteria.
Why is tracking hallucinations important?
Hallucinations can lead to misinformation. Tracking them helps improve model reliability and trustworthiness in real-world applications.