Knowledge Information Flashcards
7 cards from real AI practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Knowledge Information flashcards as text
What is the main advantage of using vector embeddings to represent knowledge compared to symbolic representations?
Answer: They capture semantic similarity and enable fuzzy matching
Vector embeddings encode semantic meaning such that similar concepts cluster together in the embedding space, enabling similarity search and generalization.
In a transformer-based language model, where is factual knowledge primarily stored?
Answer: Feed-forward network (FFN) layers
Research suggests that factual associations are predominantly encoded in the feed-forward layers of transformers, which act as key-value memories.
Which technique allows a language model to update its knowledge without full retraining?
Answer: Knowledge editing (model editing)
Knowledge editing methods like ROME or MEMIT surgically modify specific weights in a trained model to update factual associations without retraining the entire model.
What is 'entity resolution' in the context of knowledge base construction?
Answer: Identifying when different mentions refer to the same real-world entity
Entity resolution (also called entity linking or deduplication) determines that different text mentions—like 'ML' and 'machine learning'—refer to the same underlying concept or entity.
What is 'hallucination' in large language models and how does it relate to knowledge?
Answer: The model producing confident but factually incorrect statements not grounded in its training data
Hallucination occurs when a language model generates plausible-sounding but factually incorrect information, often because the query falls outside its reliable knowledge boundary.
Which graph database query language is most commonly used to query property graphs in knowledge systems?
Answer: Cypher
Cypher is the declarative query language used by Neo4j and other property graph databases, designed specifically for pattern matching over graph structures.
What is the primary purpose of a 'knowledge cutoff' date in deployed AI systems?
Answer: Indicating the date beyond which the model has no trained knowledge
The knowledge cutoff is the date after which training data was not included, meaning the model lacks awareness of events, discoveries, or changes that occurred after that date.