Want to ace your next LLM interview? Find Top 50 LLM Interview Questions Covers the topics you can’t skip 1. Core LLM Concepts ↳ Tokenization and embeddings ↳ Attention mechanism and multi-head attention ↳ Context window and positional encodings ↳ Autoregressive vs. masked models 2. Training & Fine-tuning ↳ LoRA vs. QLoRA ↳ Masked language modeling and next sentence prediction ↳ Avoiding catastrophic forgetting ↳ Model distillation and PEFT ↳ Handling out-of-vocabulary words 3. Text Generation Techniques ↳ Beam search vs. greedy decoding ↳ Temperature, top-k, and top-p sampling ↳ Chain-of-Thought prompting ↳ Zero-shot and few-shot learning 4. Optimization & Math Foundations ↳ Gradients, Jacobian matrix, and chain rule ↳ Cross-entropy loss, KL divergence ↳ Eigenvalues, eigenvectors, and dimensionality reduction ↳ ReLU derivative and vanishing gradients 5. Architectures & Extensions ↳ Seq2Seq vs. transformers ↳ Encoders vs. decoders ↳ Mixture of Experts (MoE) ↳ Retrieval-Augmented Generation (RAG) ↳ Knowledge graph integration 6. Applications & Comparisons ↳ Generative vs. discriminative models ↳ LLMs vs. traditional statistical language models ↳ GPT-4 vs. GPT-3 differences ↳ Foundation models (language, vision, multimodal) 7. Practical Considerations ↳ Hyperparameters and their role ↳ Addressing bias and incorrect outputs ↳ Deployment challenges like compute, interpretability, and privacy Comment "LLM" and I'll DM you the PDF. ♻ Repost if you found this useful.
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@_rohit_tiwari_ these are fundamentals, these are literally must
@_rohit_tiwari_ Here is the link to the PDF - drive.google.com/file/d/1xveEgw…
