In the world of Large Language Models (LLMs), understanding how text is processed under the hood is crucial. Tokenization, embeddings, and other text representation techniques form the backbone of how LLMs interpret and generate language. This blog provides a set of 10 interview questions that explore these core concepts, along with model answers and follow-up probes to help you prepare for LLM-focused interviews.
1. What is tokenization in the context of LLMs?
Focus: Understanding the first step of text preprocessing.
Standard Answer: Tokenization is the process of splitting raw text into smaller units called tokens, which could be words, subwords, or characters depending on the tokenizer. Tokenization helps models handle text in a structured way, allowing embeddings and attention mechanisms to process inputs efficiently.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- Explain the difference between word-level and subword tokenization.
- How does Byte Pair Encoding (BPE) work?
- What challenges can arise when tokenizing languages like Chinese or Arabic?
2. Explain embeddings and their role in LLMs.
Focus: Understanding vector representation of text.
Standard Answer: Embeddings are dense vector representations of tokens or text segments that capture semantic and syntactic relationships. LLMs use embeddings as inputs to the neural network, enabling operations like similarity measurement, clustering, and attention computation.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- How do embeddings differ between static and contextual representations?
- Explain how cosine similarity is used with embeddings.
- Why are embeddings usually high-dimensional vectors?
3. What are positional encodings, and why are they necessary?
Focus: Sequence information in transformer models.
Standard Answer: Positional encodings provide information about the position of tokens in a sequence, since transformer architectures do not inherently handle sequential order. They are added to token embeddings to help the model capture word order and relationships.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- Describe the difference between sinusoidal and learned positional embeddings.
- How would removing positional encodings affect a transformer’s performance?
- Can positional encodings be adapted for very long sequences?
4. What is the difference between subword and character-level tokenization?
Focus: Granularity of tokenization and handling out-of-vocabulary words.
Standard Answer: Subword tokenization splits words into meaningful units, balancing vocabulary size and expressiveness, while character-level tokenization represents every single character. Subwords reduce the number of unknown tokens and improve efficiency, while character-level can handle rare words more flexibly.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- Give an example where subword tokenization outperforms character-level.
- How does WordPiece differ from BPE?
- What are the memory implications of using character-level tokenization?
5. How do transformers use embeddings in attention mechanisms?
Focus: Linking embeddings to the core model architecture.
Standard Answer: Transformers use token embeddings combined with positional encodings as input. The attention mechanism computes queries, keys, and values from these embeddings to determine how much each token should focus on others in the sequence, enabling context-aware representations.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- Can embeddings alone capture context without attention?
- How are queries, keys, and values derived from embeddings?
- What is multi-head attention, and why is it beneficial?
6. What is the role of the vocabulary in tokenization?
Focus: Understanding the model’s textual knowledge limits.
Standard Answer: Vocabulary defines the set of tokens a model recognizes. A well-designed vocabulary ensures coverage of common words, subwords, and special tokens, while limiting size to maintain efficiency. Tokens outside the vocabulary are split into smaller units or marked as unknown.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- How does vocabulary size impact model performance?
- Explain how OOV (out-of-vocabulary) tokens are handled.
- What strategies exist to optimize vocabulary for multilingual models?
7. Describe the difference between static and contextual embeddings.
Focus: Semantic depth in vector representations.
Standard Answer: Static embeddings (e.g., Word2Vec, GloVe) assign a single vector per word regardless of context. Contextual embeddings (e.g., BERT, GPT) produce vectors that vary depending on surrounding words, capturing nuanced meaning and polysemy.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- Give an example where contextual embeddings resolve ambiguity better than static embeddings.
- How are contextual embeddings generated in transformers?
- Can static embeddings still be useful in modern NLP pipelines?
8. How does tokenization affect model performance and training efficiency?
Focus: Trade-offs in preprocessing.
Standard Answer: Tokenization determines sequence length, vocabulary coverage, and input granularity. Poor tokenization can lead to longer sequences, excessive padding, and reduced training efficiency. Optimized tokenization balances model accuracy, memory usage, and speed.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- What are the performance implications of using smaller vs. larger vocabularies?
- How does subword tokenization help with rare words?
- Can tokenization introduce bias in language models?
9. Explain embedding fine-tuning in LLMs.
Focus: Adapting pre-trained embeddings for downstream tasks.
Standard Answer: Fine-tuning embeddings involves updating the vectors of tokens based on task-specific data. This allows the model to learn domain-specific nuances and improve performance on tasks such as classification, question answering, or generation.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- What is the difference between full model fine-tuning and embedding fine-tuning?
- How can catastrophic forgetting be mitigated during fine-tuning?
- When would you prefer frozen embeddings over trainable embeddings?
10. How do tokenization and embeddings interplay in multilingual LLMs?
Focus: Cross-lingual representation and model design.
Standard Answer: In multilingual LLMs, tokenization must handle diverse scripts and token frequencies. Embeddings map these tokens into a shared vector space, allowing cross-lingual understanding. Subword tokenization is often preferred to manage large vocabularies efficiently.
Possible 3 follow-up questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)
- How do multilingual embeddings handle languages with few training resources?
- Explain challenges in shared vs. language-specific vocabularies.
- What strategies improve alignment of embeddings across languages?
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