Compare NLP tokenization and LLM recommendations
Company: Google
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
You’re interviewing for an NLP-focused ML role.
## Part A — NLP fundamentals: tokenization
Explain and compare common tokenization approaches used in modern NLP/LLMs:
- Word-level tokenization
- Character-level tokenization
- Subword tokenization families (e.g., BPE/WordPiece/Unigram/SentencePiece)
Discuss trade-offs and when you would choose each, considering:
- OOV (out-of-vocabulary) handling
- Vocabulary size vs. sequence length
- Multilingual and morphologically rich languages
- Training/serving efficiency and memory
- Robustness to typos, rare words, and domain terms
## Part B — Mini case: using an LLM for recommendation
Design an approach to use an LLM to improve a recommender system (e.g., e-commerce content or item recommendations).
Cover:
- What role(s) the LLM plays (candidate generation, ranking, re-ranking, feature generation, explanations, conversational recs)
- What data you would use (user history, item metadata, text reviews, session signals)
- How you would evaluate the approach (offline + online), and key risks (hallucination, bias, latency/cost, privacy).
Quick Answer: This question evaluates a candidate's understanding of NLP tokenization approaches and the ability to design LLM-based recommendation components, assessing competencies in trade-offs among word/character/subword tokenization, OOV and multilingual handling, and roles LLMs can play in recommendation pipelines.