llm-evaluation-metrics
CommunityValidate LLM performance, ensure quality.
Data & Analytics#summarization#A/B testing#metrics#RAG#classification#LLM evaluation#human evaluation
Authortachyon-beep
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a comprehensive framework for evaluating LLM performance across various tasks (classification, generation, RAG, summarization, chat). It ensures you use the right metrics, combine automated and human evaluation, and conduct rigorous A/B testing, preventing you from shipping underperforming or unsafe LLM applications.
Core Features & Use Cases
- Task-Specific Metric Selection: Choose appropriate metrics like F1, BLEU, ROUGE, BERTScore, MRR, and Faithfulness based on your LLM's function.
- Human Evaluation Protocol: Design robust human evaluation studies to assess fluency, relevance, helpfulness, and safety, capturing nuances automated metrics miss.
- Use Case: You've fine-tuned an LLM for customer support and need to prove its effectiveness. This skill guides you to set up an A/B test, define key business metrics (CSAT, completion rate), and perform statistical significance testing before full deployment.
Quick Start
I need to evaluate my LLM's performance for a summarization task. What metrics should I use?
Dependency Matrix
Required Modules
scikit-learnnumpynltkrougebert-scoretorchtransformersscipy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-evaluation-metrics Download link: https://github.com/tachyon-beep/skillpacks/archive/main.zip#llm-evaluation-metrics Please download this .zip file, extract it, and install it in the .claude/skills/ directory.