babelcloud/LLM-RGB

LLM Reasoning and Generation Benchmark. Evaluate LLMs in complex scenarios systematically.

41
/ 100
Emerging

This project offers a collection of detailed test cases (prompts) to evaluate how well Large Language Models (LLMs) can reason and generate responses in complex scenarios. It takes in various LLMs and returns a performance score based on how accurately they follow instructions, handle long contexts, and perform multi-step reasoning. This is for AI/ML engineers, product managers, or researchers who need to rigorously assess LLM capabilities beyond simple chat interactions.

166 stars. No commits in the last 6 months.

Use this if you need to systematically benchmark different LLMs or monitor the performance of your LLM in real-world applications that involve lengthy inputs, intricate logic, or strict output formats.

Not ideal if you are looking for a comprehensive, all-encompassing LLM benchmark or a tool to evaluate simple, conversational AI interactions.

LLM evaluation AI model benchmarking AI quality assurance LLM performance testing prompt engineering
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 13 / 25

How are scores calculated?

Stars

166

Forks

16

Language

TypeScript

License

MIT

Last pushed

May 25, 2025

Commits (30d)

0

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