Compare NLP tokenization and LLM recommendations

Q: Compare NLP tokenization and LLM recommendations

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.

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Question

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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).

Compare NLP tokenization and LLM recommendations

Quick Overview

Part A — NLP fundamentals: tokenization

Part B — Mini case: using an LLM for recommendation

Solution

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