Llama LLM MCQ Questions and Answers

Q1. What is Retrieval-Augmented Generation (RAG)?

A. A technique for generating text without any external knowledge
B. A method that combines retrieval-based and generation-based approaches for text generation
C. A framework for training language models exclusively on retrieved documents
D. An approach that replaces language models with knowledge retrieval systems

Q2. What problem does RAG primarily aim to solve?

A. The inability of language models to generate coherent text
B. The outdated knowledge in pre-trained language models
C. The slow processing speed of large language models
D. The limited creativity of generative AI systems

Q3. Which of the following is a key component of a RAG system?

A. A vector database for storing embeddings
B. A web crawler for finding new information
C. A reinforcement learning module
D. A hardware acceleration unit

Q4. How does RAG improve over traditional language models?

A. By significantly increasing the parameter count of the model
B. By eliminating the need for a language model entirely
C. By enhancing factuality and reducing hallucinations
D. By focusing solely on generating creative content

Q5. Which embedding technique is commonly used in RAG systems?

A. One-hot encoding
B. Dense vector embeddings
C. Binary encodings
D. Random projections

Q6. What is the role of a retriever in a RAG system?

A. To generate responses based on user queries
B. To parse and preprocess user input
C. To find and retrieve relevant information from a knowledge base
D. To evaluate the quality of generated responses

Q7. When implementing a RAG pipeline, what metric is commonly used to evaluate retrieval quality?

A. Perplexity
B. Recall@k
C. BLEU score
D. F1 score

Q8. What distinguishes hybrid search from pure vector search in RAG systems?

A. Hybrid search only uses keywords while vector search uses embeddings
B. Hybrid search combines vector search with traditional keyword-based approaches
C. Hybrid search is significantly slower but more accurate
D. Hybrid search only works with structured data

Q9. What is the purpose of re-ranking in a RAG pipeline?

A. To compress retrieved documents for faster processing
B. To further refine the initial retrieval results for better relevance
C. To translate retrieved documents into the target language
D. To create summaries of retrieved documents

Q10. What is a common challenge in RAG systems related to document chunking?

A. Ensuring chunks are exactly the same size
B. Finding the optimal chunk size to balance context preservation and specificity
C. Eliminating all overlap between chunks
D. Converting all chunks to the same file format

Q11. What is the role of prompt engineering in a RAG system?

A. To automatically generate user queries
B. To design effective ways to incorporate retrieved information into LLM instructions
C. To create embeddings for vector search
D. To compress the knowledge base

Q12. What is the purpose of the "augmentation" step in RAG?

A. To enhance the training data for the language model
B. To increase the size of the knowledge base
C. To supplement the LLM's prompt with relevant retrieved information
D. To improve the visual presentation of responses

Q13. Which RAG architecture allows for interactively making multiple queries to a knowledge base during response generation?

A. Single-hop RAG
B. Multi-hop RAG
C. Recursive RAG
D. Parallel RAG

Q14. What is a key advantage of using RAG over fine-tuning a language model on domain-specific data?

A. RAG is always faster at inference time
B. RAG doesn't require any preprocessing of documents
C. RAG allows for easier updates to the knowledge base without retraining
D. RAG eliminates the need for a language model entirely

Q15. What is a common technique for evaluating the factual accuracy of RAG system outputs?

A. A/B testing with users
B. Fact-checking against the retrieved documents
C. Measuring token generation speed
D. Analyzing grammar correctness

Q16. What is "hallucination" in the context of RAG systems?

A. When the retrieval system returns irrelevant documents
B. When the language model generates content not supported by retrieved information
C. When the vector database crashes unexpectedly
D. When the embeddings fail to capture semantic meaning

Q17. What is "query expansion" in RAG systems?

A. Increasing the size of the database with each query
B. Generating multiple varied versions of the original query to improve retrieval
C. Making queries more complex to test system performance
D. Expanding the LLM's token limit for processing longer queries

Q18. Which technique helps address the context window limitation when dealing with many retrieved documents?

A. Document summarization
B. Increasing model size
C. Limiting retrieval to a single document
D. Converting text to images

Q19. What is "recursive retrieval" in advanced RAG architectures?

A. Using the same documents repeatedly
B. Using the generated output as a query for additional retrievals
C. Retrieving documents from multiple databases simultaneously
D. Indexing the same documents with different embedding models

Q20. Which metric measures how often a RAG system's output contains information not supported by retrieved documents?

A. Perplexity
B. BLEU score
C. Hallucination rate
D. Retrieval latency

Q21. Which of the following is a common approach for calculating document relevance in RAG systems?

A. Document length
B. Publication date
C. Cosine similarity between query and document embeddings
D. File format

Q22. What is "retrieval chain of thought" in RAG?

A. Retrieving documents sequentially from oldest to newest
B. A technique where the model explains its retrieval process step by step
C. Linking multiple retrieval systems together
D. The physical connection between database servers

Q23. What is the purpose of document filtering in a RAG pipeline?

A. To convert documents to a common file format
B. To exclude irrelevant documents before more detailed processing
C. To translate documents into different languages
D. To compress documents for faster transmission

Q24. Which of the following is NOT typically a component of a RAG architecture?

A. Document retriever
B. Large language model
C. Reinforcement learning trainer
D. Vector database

Q25. What is "dense passage retrieval" in the context of RAG?

A. Retrieving only the longest documents in the database
B. A technique using dense vector representations for efficient retrieval
C. Compressing documents to save storage space
D. Retrieving all documents simultaneously

Q26. What is "knowledge distillation" in the context of RAG?

A. Compressing the size of the knowledge base
B. Transferring knowledge from retrieved documents to the language model
C. Creating summaries of retrieved documents
D. Training a smaller model to mimic a larger one

Q27. Which approach can help address the "lost in the middle" problem in RAG systems?

A. Using shorter documents
B. More aggressive document chunking
C. Retrieving a larger number of documents
D. Highlighting important information within documents

Q28. What is the advantage of using sentence-level embeddings over document-level embeddings in RAG?

A. They require less storage space
B. They provide more fine-grained retrieval
C. They are faster to generate
D. They use less memory during inference

Q29. What is "retrieval fusion" in RAG systems?

A. Merging multiple knowledge bases into one
B. Combining retrieved documents to create a single document
C. Integrating results from multiple retrieval methods
D. Fusing the retriever and generator into a single model

Q30. What is a key consideration when deciding how many documents to retrieve in a RAG system?

A. The LLM's context window size
B. The number of users accessing the system
C. The creation date of the documents
D. The file format of the documents

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