Large Language Model (LLM) Interview Questions Quiz

Test your understanding of Large Language Models (LLMs) with this SEO-friendly quiz. Explore fundamental LLM interview questions covering tokenization, attention mechanisms, fine-tuning techniques, context windows, and more key concepts relevant for AI professionals and enthusiasts.

1. Question 1

   What is the main purpose of tokenization in Large Language Models (LLMs)?

   1. To add random noise for model regularization
   2. To increase model parameters for better precision
   3. To break down raw text into smaller units like words, subwords, or characters
   4. To translate text between different languages

   Explanation: Tokenization is essential to LLMs because it converts raw text into manageable tokens (words, subwords, or characters), allowing the model to work efficiently with numeric representations. Increasing model parameters does not achieve tokenization. Translation is related to application, not pre-processing. Adding random noise is a data augmentation method, not tokenization.

2. Question 2

   How does the attention mechanism enhance transformer models during text processing?

   1. By weighing the importance of different tokens within a sequence
   2. By translating tokens into embeddings
   3. By compressing the input data for speed
   4. By randomizing the word order in a sentence

   Explanation: The attention mechanism allows models to focus on specific tokens that matter, improving context understanding and relevance in outputs. Randomizing word order would hinder comprehension. Translating tokens into embeddings is separate from attention. Compressing input speeds up processing but is not what attention does.

3. Question 3

   What does the context window refer to in LLMs, and why is it significant?

   1. The batch size used during model training
   2. The format of output text generated by the model
   3. The maximum number of tokens the model can process at once, influencing coherence and performance
   4. A tool for measuring training dataset quality

   Explanation: The context window is crucial as it determines how much of the input or conversational history the LLM can consider, directly impacting its coherence and ability to generate contextually accurate responses. Output format and dataset quality tools are unrelated. Batch size is a separate training concept.

4. Question 4

   What is a key difference between LORA and QLORA in fine-tuning LLMs?

   1. QLORA uses quantization in addition to low-rank adaptation for further memory reduction
   2. LORA relies solely on random noise for adaptation
   3. QLORA doesn't use any matrices for adaptation
   4. LORA incorporates image data during training

   Explanation: QLORA builds on LORA by integrating quantization, which reduces the computational and memory requirements while maintaining efficiency. LORA does not involve random noise or images in its basic approach. QLORA, on the contrary, still uses matrices—its distinction is quantization.

5. Question 5

   In text generation, how does beam search differ from greedy decoding?

   1. Beam search keeps multiple top candidate sequences at each step, unlike greedy decoding which chooses only the most probable word
   2. Beam search compresses input before generating output
   3. Beam search always chooses the least likely word at every step
   4. Greedy decoding produces more diverse text options than beam search

   Explanation: Beam search considers multiple likely word sequences, enhancing coherence and diversity in generated texts, whereas greedy decoding selects only the most probable option. It does not always choose the least likely words nor is its goal to compress input data. Greedy decoding tends to be less diverse than beam search.

6. Question 6

   What is the effect of adjusting the temperature parameter during LLM text generation?

   1. It controls the randomness and diversity of output by modifying token probability distribution
   2. It changes the input sentence structure
   3. It increases the model's training speed
   4. It adjusts the number of layers in the model dynamically

   Explanation: Temperature directly affects the randomness of token selection, allowing for either more deterministic or more diverse outputs. It does not impact model speed, structure, or input sentence organization. This hyperparameter helps balance creativity and coherence.

7. Question 7

   How does masked language modeling (MLM) help pretrain Large Language Models?

   1. By using only forward context to predict the next word
   2. By hiding some tokens during training and teaching the model to predict them using context
   3. By sorting tokens based on frequency
   4. By deleting the shortest sentences from the dataset

   Explanation: MLM works by masking certain tokens in the input and training the model to recover them, encouraging a deep understanding of context and relationships. Deleting sentences or sorting tokens isn't MLM. Using only forward context describes autoregressive, not masked, modeling.

8. Question 8

   Where are sequence-to-sequence (Seq2Seq) models commonly applied?

   1. Machine translation, text summarization, and chatbots handling variable-length input and output
   2. Sorting numbers in ascending sequence
   3. Detecting hardware errors in computer systems
   4. Classifying images into categories

   Explanation: Seq2Seq models excel at tasks that require transforming an input sequence into a different output sequence, such as translation or summarization. Image classification and hardware error detection are unrelated tasks, and sorting numbers does not require a Seq2Seq approach.

9. Question 9

   What distinguishes autoregressive models from masked models in LLM training?

   1. Autoregressive models only analyze images, while masked models process text
   2. Masked models cannot be pre-trained
   3. Autoregressive models are incapable of generating text
   4. Autoregressive models predict tokens one-by-one based on previous tokens; masked models predict hidden tokens using context

   Explanation: Autoregressive models are generative, predicting the next token from history, while masked models fill in masked tokens using surrounding context. Both work on text, not exclusively images. Masked models are often pre-trained, and autoregressive models are commonly used for text generation.

10. Question 10

    What is one primary advantage of using quantization when fine-tuning large language models?

    1. It guarantees no loss in model performance
    2. It reduces memory requirements and computational load while maintaining reasonable accuracy
    3. It increases the number of output tokens
    4. It eliminates the need for tokenization

    Explanation: Quantization lowers bit precision, allowing large models to run on limited hardware with smaller memory and computational demands, usually with minimal accuracy loss. It does not change the number of output tokens, nor does it guarantee perfect accuracy. Tokenization remains necessary.

11. Question 11

    When configuring a transformer model for a very long document, what will increasing the context window size most likely affect?

    1. It will enable the model to consider more tokens simultaneously, but increase computational cost
    2. It reduces the need for attention mechanisms
    3. It will decrease the vocabulary size
    4. It validates model predictions automatically

    Explanation: A larger context window allows the model to analyze longer text spans at once but consumes more computational resources. Vocabulary size adjustments are separate, and predictions aren't validated purely by context size. Attention mechanisms are still necessary and not reduced.

12. Question 12

    In practice, why might a developer select a low temperature value like 0.2 for text generation with an LLM?

    1. To produce more predictable and repetitive outputs by favoring high-probability tokens
    2. To randomly eliminate words from the output
    3. To make generated text as diverse as possible
    4. To increase the number of layers in the neural network

    Explanation: A low temperature narrows the probability distribution, leading to more repetitive and deterministic outputs. High diversity is achieved with higher temperatures. Random elimination of words and neural network layers are unrelated to temperature settings.

13. Question 13

    Why is bidirectional context important for masked language models?

    1. It increases training errors intentionally
    2. It enables the model to use information from both past and future tokens to better predict masked tokens
    3. It allows models to process images and videos
    4. It restricts the model to only use past context

    Explanation: Bidirectional context helps the model leverage information from both sides of a masked token, boosting accuracy and meaning extraction. Restricting to past context would limit understanding, and increased errors or processing images are not connected to this feature.

14. Question 14

    How does the encoder-decoder architecture benefit Seq2Seq models in NLP applications?

    1. The encoder processes input sequences and the decoder generates output sequences, enabling variable-length transformation
    2. It always generates images from textual input
    3. It reduces vocabulary size by half
    4. It sorts numbers from highest to lowest

    Explanation: The encoder-decoder setup supports transforming sequences of one type and length to another, which is key in translation and summarization. The other options do not relate to Seq2Seq architecture and instead describe unrelated processing or structural changes.

15. Question 15

    What is a typical use case for utilizing beam search over greedy decoding in LLM outputs?

    1. When seeking more coherent and contextually accurate text sequences by evaluating multiple candidates
    2. When the model needs to reduce the size of its vocabulary
    3. To randomly select tokens without considering probabilities
    4. For analyzing audio signals instead of text

    Explanation: Beam search helps produce coherent and contextually appropriate spoken or written text by considering several options at each step. Beam search is not for vocabulary reduction, non-text analysis, or randomness without probability guidance.