A. To combine multiple components into a single pipeline
B. To train Large Language Models
C. To break down complex tasks into smaller steps
D. To retrieve relevant information from knowledge bases
A. It penalizes only tokens that have never appeared in the text before.
B. It applies a penalty only if the token has appeared more than twice.
C. It penalizes all tokens equally, regardless of how often they have appeared.
D. It penalizes a token each time it appears after the first occurrence.
A. After user input but before chain execution, and again after core logic but before output
B. Continuously throughout the entire chain execution process
C. Before user input and after chain execution
D. Only after the output has been generated
A. Python's class and object structures
B. Python's lambda functions
C. Python's list comprehension syntax
D. Python's str.format syntax
A. The token is more likely to follow the current token.
B. The token is unrelated to the current token and will not be used.
C. The token is less likely to follow the current token.
D. The token will be the only one considered in the next generation step.
A. Capacity to translate text in over u languages
B. Emphasis on syntactic clustering of word embedding's
C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
D. Support for tokenizing longer sentences
A. Top p determines the maximum number of tokens per response.
B. Top p limits token selection based on the sum of their probabilities.
C. Top p selects tokens from the "Top k' tokens sorted by probability.
D. Top p assigns penalties to frequently occurring tokens.
