Measure over-refusal in LLMs using OR-Bench
Compare and rank LLMs using benchmark scores
Benchmark LLMs in accuracy and translation across languages
Upload a machine learning model to Hugging Face Hub
Measure execution times of BERT models using WebGPU and WASM
Calculate VRAM requirements for LLM models
View NSQL Scores for Models
SolidityBench Leaderboard
Evaluate open LLMs in the languages of LATAM and Spain.
Measure BERT model performance using WASM and WebGPU
Generate and view leaderboard for LLM evaluations
Display and filter leaderboard models
Teach, test, evaluate language models with MTEB Arena
OR-Bench Leaderboard is a tool designed to measure and benchmark over-refusal in Large Language Models (LLMs). It provides a standardized framework to evaluate when and how models refuse to answer questions or generate text. This leaderboard helps researchers and developers understand the limitations and safety mechanisms of LLMs by comparing their performance across different scenarios.
• Model Comparison: Allows users to compare multiple models based on their refusal patterns.
• Refusal Trigger Evaluation: Tests models against a curated set of triggers to assess their refusal thresholds.
• Metric Aggregation: Provides aggregated metrics such as refusal rates and response patterns.
• Result Sharing: Enables sharing of benchmark results for community collaboration.
• Documented Methodology: Offers transparent documentation of evaluation methods and datasets.
1. What is the purpose of OR-Bench Leaderboard?
The purpose of OR-Bench Leaderboard is to provide a standardized way to measure and compare over-refusal behaviors in LLMs, helping to identify models with balanced safety and utility.
2. Why is measuring over-refusal important?
Measuring over-refusal is important because it helps assess a model's ability to avoid harmful or inappropriate responses while still providing useful answers.
3. How can I interpret the results from OR-Bench Leaderboard?
Results show how often a model refuses to answer and under what conditions. Lower refusal rates may indicate a model that is more permissive, while higher rates suggest stricter safety mechanisms.