SkillAI · The Beam

LLM Assessment Mastery

0/30 concepts·~13 h left

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Foundational Concepts of LLMs

Understanding LLMs and their architecture25 min
Key NLP concepts: tokens, embeddings, etc.25 min
Introduction to transformers and attention mechanisms25 min
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Evaluation Metrics for LLMs

Common metrics: BLEU, ROUGE, F125 min
Understanding perplexity and its implications25 min
Human evaluation methods: A/B testing and surveys25 min
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Data Preparation for Testing

Collecting and cleaning datasets for evaluation25 min
Creating benchmark datasets for LLMs25 min
Understanding data leakage and its impact25 min
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Prompt Engineering Techniques

Crafting effective prompts for GPT-3 and Claude25 min
Using LangChain for prompt management25 min
Evaluating prompt effectiveness with human feedback25 min
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Testing Frameworks and Tools

Introduction to Hugging Face transformers library25 min
Using Weights & Biases for experiment tracking25 min
Setting up MLflow for model management25 min
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Bias and Fairness Evaluation

Identifying bias in LLM outputs25 min
Using Fairness Indicators for evaluation25 min
Mitigating bias through data and model adjustments25 min
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Robustness Testing of LLMs

Adversarial testing techniques for LLMs25 min
Evaluating model performance under stress tests25 min
Using TextAttack for adversarial examples25 min
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Real-World Application Scenarios

Case studies of LLMs in industry applications25 min
Evaluating LLMs for customer support automation25 min
Testing LLMs for content generation and summarization25 min
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Advanced Evaluation Techniques

Using GPT-4 for zero-shot evaluation25 min
Implementing self-supervised evaluation methods25 min
Leveraging active learning for continuous improvement25 min
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Continuous Learning and Staying Current

Following key research papers and conferences25 min
Participating in online communities and forums25 min
Contributing to open-source projects in LLM evaluation25 min