In data science and natural language processing engineering interviews, you may be asked a variety of questions to assess your knowledge and skills related to large language models. These interviews typically include a variety of questions designed to assess your proficiency in handling large language models, understanding NLP fundamentals, and mastering the intricacies of Transformer-based architectures.
In this article, we’ll dive into the basic interview questions you might face in a data science and NLP engineering interview. We divide these questions into three different sections, each focusing on a different aspect of NLP and large language models: NLP basics, Transformer-based queries, and queries specific to large language models.
Table of contents:
1. NLP basic interview questions and answers
1.1. Describe the process of tokenization in NLP. Why is it important? What challenges arise during the tokenization process?
1.2. How do you deal with bias in NLP models? What techniques can be used to mitigate this problem?
1.3. What are the key evaluation metrics for evaluating the performance of NLP models, especially in tasks such as text classification and machine translation?
1.4. Explain the concept of word embeddings and how they are used in NLP models such as Word2Vec and GloVe.
1.5. Explain the concept of transfer learning in NLP and its importance in building effective NLP models.
1.6. Can you discuss some common techniques for handling out-of-vocabulary (OOV) words in NLP tasks?
1.7. How to choose the appropriate architecture or model size for a specific NLP task?
1.8. What are the differences between supervised learning, unsupervised learning and semi-supervised learning in NLP? When would you use each method?
1.9. In the context of NLP, what are the common techniques for text data preprocessing and cleaning?
1.10. Describe some popular libraries and frameworks used to handle NLP tasks, such as spaCy, NLTK or Hugging Face Transformers.
1.11. Explaining NLP