AI Knowledge Information 3

Question 1

What is the main advantage of using vector embeddings to represent knowledge compared to symbolic representations?

Accepted Answer

They capture semantic similarity and enable fuzzy matching

Answer

Vector embeddings encode semantic meaning such that similar concepts cluster together in the embedding space, enabling similarity search and generalization.

Question 2

In a transformer-based language model, where is factual knowledge primarily stored?

Accepted Answer

Feed-forward network (FFN) layers

Answer

Research suggests that factual associations are predominantly encoded in the feed-forward layers of transformers, which act as key-value memories.

Question 3

Which technique allows a language model to update its knowledge without full retraining?

Accepted Answer

Knowledge editing (model editing)

Answer

Knowledge editing methods like ROME or MEMIT surgically modify specific weights in a trained model to update factual associations without retraining the entire model.

Question 4

What is 'entity resolution' in the context of knowledge base construction?

Accepted Answer

Identifying when different mentions refer to the same real-world entity

Answer

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.

Question 5

What is 'hallucination' in large language models and how does it relate to knowledge?

Accepted Answer

The model producing confident but factually incorrect statements not grounded in its training data

Answer

Hallucination occurs when a language model generates plausible-sounding but factually incorrect information, often because the query falls outside its reliable knowledge boundary.

AI - Engineer Practice Test

AI - Engineer Practice Test

AI Knowledge Information 3