Introduction to Large Language Models
UPDATED ACTUAL Exam Questions and
CORRECT Answers
large language models - CORRECT ANSWER - large, general-purpose language models can
be pre-trained (basic common language problems: text classification, question answering,
document summarization, text generation) and then fine-tuned (specific problems in different
fields) specific purposes
3 major features of large language models - CORRECT ANSWER - 1. large:
-large training dataset
- a large number of parameters: memory and knowledge machine-learned from training, define
the skills of the model
2. general purpose: model sufficient ability to solve a common problem
-commonality of human languages
-resource restriction
3. pre-trained and fine-tuned
benefits of using large language models - CORRECT ANSWER - 1. a single code can be used
for different tasks
2. the fine-tuning process requires minimal field data
-obtain decent performance even with minimal training
-can be used for few-shot or even zero-shot scenarios
3. the performance of LLM is continuously growing when you add more data and parameters
, few-shot - CORRECT ANSWER - in machine learning, it refers to training a model with
minimal data
zero-shot - CORRECT ANSWER - in Machine learning, implies that a model can recognize
things that have not explicitly been taught in the training before
Pathways Language Model (PaLM) - CORRECT ANSWER - -has 540 billion parameters
-leverages the new Pathway system: efficiently train a single model across multiple TPU v4 Pods
-orchestrate distributed computation for accelerators
*will handle many tasks at once, learn new tasks quickly, and reflect a better understanding of
the world
Transformer model - CORRECT ANSWER - 1. encoding component
-input
2. decoding component
-output
LLM Development v. Traditional Development - CORRECT ANSWER - LLM development
-no ML expertise needed
-no training examples
-no need to train a model
-thinks about prompt design
Traditional ML development
-yes ML expertise needed
-yes training examples
yes need to train a model
UPDATED ACTUAL Exam Questions and
CORRECT Answers
large language models - CORRECT ANSWER - large, general-purpose language models can
be pre-trained (basic common language problems: text classification, question answering,
document summarization, text generation) and then fine-tuned (specific problems in different
fields) specific purposes
3 major features of large language models - CORRECT ANSWER - 1. large:
-large training dataset
- a large number of parameters: memory and knowledge machine-learned from training, define
the skills of the model
2. general purpose: model sufficient ability to solve a common problem
-commonality of human languages
-resource restriction
3. pre-trained and fine-tuned
benefits of using large language models - CORRECT ANSWER - 1. a single code can be used
for different tasks
2. the fine-tuning process requires minimal field data
-obtain decent performance even with minimal training
-can be used for few-shot or even zero-shot scenarios
3. the performance of LLM is continuously growing when you add more data and parameters
, few-shot - CORRECT ANSWER - in machine learning, it refers to training a model with
minimal data
zero-shot - CORRECT ANSWER - in Machine learning, implies that a model can recognize
things that have not explicitly been taught in the training before
Pathways Language Model (PaLM) - CORRECT ANSWER - -has 540 billion parameters
-leverages the new Pathway system: efficiently train a single model across multiple TPU v4 Pods
-orchestrate distributed computation for accelerators
*will handle many tasks at once, learn new tasks quickly, and reflect a better understanding of
the world
Transformer model - CORRECT ANSWER - 1. encoding component
-input
2. decoding component
-output
LLM Development v. Traditional Development - CORRECT ANSWER - LLM development
-no ML expertise needed
-no training examples
-no need to train a model
-thinks about prompt design
Traditional ML development
-yes ML expertise needed
-yes training examples
yes need to train a model
