Intro to NLP End Term: 13 April 2025 (January 2025 term)

Because accuracy does not account for the number of true negatives.

Because accuracy gives equal weight to all classes, even if some tags are much rarer than others.

Because accuracy only measures the precision of the system, ignoring recall.

Because accuracy is not affected by the number of false positives and false negatives.

Individual word probabilities are sufficient for accurate tagging, and tag sequence probabilities are redundant.

Individual word probabilities are only used for rare words, while tag sequence probabilities are used for common words.

Tag sequence probabilities help in determining the most likely sequence of tags, while individual word probabilities provide local context.

Both probabilities are used to reduce the computational complexity of the tagging process.

RAG eliminates the need for transformer-based token generation.

RAG uses retrieval but does not update its knowledge dynamically over time.

RAG only works when trained on labeled question-answer datasets.

RAG retrieves external documents dynamically, reducing reliance on pre- trained knowledge.