Explain LLM Concepts for Learners

Business Context

LearnLoop, an online technical education platform, wants an NLP system that can explain how large language models work in language appropriate for different audiences such as middle-school students, non-technical professionals, and junior engineers. The goal is to generate accurate, readable explanations while also labeling each explanation by concept coverage so curriculum teams can audit quality.

Data

• Volume: 180,000 instructional text snippets, FAQ entries, model cards, and human-written explanations
• Text length: 40-900 words per document; generated outputs should be 120-300 words
• Language: English only
• Labels: 6 concept tags with multi-label distribution: tokenization, embeddings, attention, training, inference, limitations
• Class balance: Uneven; attention and tokenization appear frequently, limitations less often

Success Criteria

• Generated explanations must be factually correct and readable for the target audience
• Multi-label concept classifier should achieve macro-F1 >= 0.84
• Explanation generation should achieve strong human ratings on clarity and correctness
• End-to-end response latency should stay under 1.5 seconds for a single request

Constraints

• Must run on a single A10G GPU in a private environment
• No external API calls at inference time
• Explanations must avoid unsupported claims and clearly state limitations of LLMs

Requirements

1. Build a system that generates audience-specific explanations of LLM mechanics
2. Add a multi-label classifier to verify which concepts are covered in each explanation
3. Define a preprocessing pipeline for educational and technical text
4. Implement a modern Python solution using Hugging Face Transformers
5. Propose evaluation for factuality, readability, concept coverage, and latency
6. Describe trade-offs between a single generative model and a generate-then-classify pipeline