Generative AI and GPT Overview

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Questions and Answers

Which of the following tasks can generative AI perform?

Only content translation

Performing arithmetic calculations

Generating graphics and images

Answering open-ended questions (correct)

What is a key characteristic of LLMs (Large Language Models)?

They operate without any training data

They generate multimedia outputs

They require extensive graphical data

They create text-only outputs (correct)

Which of these generative AI tools is based on LLM technology?

Photoshop

Logic Pro

ChatGPT (correct)

DALL-E

What does the term 'bidirectional representation learning' refer to in the context of BERT?

Understanding context from both sides of a word Signup and view all the answers

Which of the following is NOT a function attributed to generative AI?

Simulating human consciousness Signup and view all the answers

Which feature distinguishes GPT from other generative AI models mentioned?

It focuses primarily on text generation Signup and view all the answers

In the context of GPT architecture, what does STF stand for?

Supervised Fine-Tuning Signup and view all the answers

What differentiates BERT from GPT?

BERT facilitates bidirectional representation learning. Signup and view all the answers

What era did the Ceratosaurus inhabit?

Late Jurassic Signup and view all the answers

How tall is Roxy Ann Peak?

3,576 feet Signup and view all the answers

What is the purpose of generating synthetic questions using a seq2seq model?

To create training data for models Signup and view all the answers

What is a characteristic of 'strong negatives' in question generation?

They are positive questions from the same paragraph Signup and view all the answers

What is one disadvantage of the two-pass training method?

Incompatibility with non-pre-trained models Signup and view all the answers

What role does the baseline SQuAD 2.0 system play in question generation?

It filters out low-quality questions Signup and view all the answers

Which method enhances the predictions made by right-to-left SQuAD models?

Training a two-model system Signup and view all the answers

What is a key potential problem when integrating right-to-left and left-to-right predictions?

Incompatibility in word prediction Signup and view all the answers

What function is used in the dense layer that creates the reset gate in GRU computations?

Sigmoid Signup and view all the answers

What does the reset gate, rt, determine in the hidden state update process?

How much of the previous hidden state is carried forward Signup and view all the answers

What is the range of values stored in the vector h˜t representing the new beliefs of the cell?

-1 to 1 Signup and view all the answers

Which gate in GRU computations helps to determine how much of the new beliefs to blend into the current hidden state?

Update gate Signup and view all the answers

What is the output of the GRU cell after updating the hidden state?

Updated hidden state, ht Signup and view all the answers

Which activation function is used for generating the new beliefs of the cell in GRU computations?

Tanh Signup and view all the answers

What dimensions does the vector zt, created by the update gate, have?

Equal to the number of units in the cell Signup and view all the answers

What does the blending process involving zt and ht-1 produce?

New hidden state, ht Signup and view all the answers

What is the primary function of an attention mechanism in a Transformer?

To focus on certain words while largely ignoring others. Signup and view all the answers

How do attention heads differ from recurrent layers in handling context?

Attention heads can choose how to combine information based on the task at hand. Signup and view all the answers

What does the query represent in the context of the attention mechanism?

A specific word that triggers a search for related context. Signup and view all the answers

What are 'keys' and 'values' used for in the attention mechanism?

For weighting input data based on its relevance to the query. Signup and view all the answers

Which of the following statements about the attention mechanism is NOT true?

It relies on sequential processing of words. Signup and view all the answers

What happens to the output of a query in the Transformer architecture?

It is a weighted sum of values based on key resonance. Signup and view all the answers

What advantage do attention heads provide over recurrent layers in language processing?

They can dynamically focus on different words based on relevancy. Signup and view all the answers

Which of the following illustrates how an attention mechanism functions?

Generating a response based on weighted relationships between words. Signup and view all the answers

What type of sentences does the CoLa dataset primarily analyze?

Acceptable and unacceptable sentences Signup and view all the answers

Which dataset contains around 108k questions sourced from Wikipedia?

SQuAD Signup and view all the answers

What is the main function of the token 0 ([CLS]) in SQuAD 2.0?

To indicate the presence of an answer Signup and view all the answers

What type of reasoning does the SWAG dataset primarily evaluate?

Commonsense reasoning Signup and view all the answers

What is the key parameter introduced in SQuAD 1.1 to enhance performance?

Start vector Signup and view all the answers

What distinguishes the Masked LM approach from Left-to-right LM in pre-training tasks?

The sequence of predictions Signup and view all the answers

How does the SWAG dataset process each premise and ending pair through BERT?

By applying a softmax layer Signup and view all the answers

What does the threshold optimization in SQuAD 2.0 aim to improve?

The accuracy of the no answer logit Signup and view all the answers

Study Notes

Generative AI

Not all forms of generative AI are built on large language models (LLMs), but all LLMs are a form of generative AI
LLMs exclusively produce text outputs
LLMs are continuously developing
ChatGPT and Google's Bard are prominent examples of LLMs
LLMs are a type of deep learning algorithm that are trained on massive datasets of text and code to generate human-like text

GPT

GPT stands for Generative Pre-trained Transformer
GPT is a type of LLM that is trained on a massive dataset of text to generate human-like text
GPT can be fine-tuned for various tasks, including translation, text summarization, and question answering
GPT can produce text, translate languages, write different kinds of creative content, and answer your questions in an informative way

GPT Architecture

The GPT architecture uses a transformer network
GPT is a decoder-only architecture, similar to the decoder in the encoder-decoder model, which only processes the input sequence once
GPT is pre-trained on a massive dataset of text before being fine-tuned for specific tasks
This pre-training allows the model to learn general language representations
BERT-GPT and other models have extended the original GPT architecture, adding new capabilities

### BERT

BERT (Bidirectional Encoder Representations from Transformers) is a technique for natural language processing pre-training
BERT is a bidirectional model, meaning the model can process the input sequence in both directions
BERT is trained on a masked language modeling task, where the model tries to predict masked words in a sentence
BERT's performance on SQuAD (Stanford Question Answering Dataset) resulted in a significant breakthrough, surpassing previous techniques in accuracy and achieving human-level performance.
The model can predict answer spans from a lot of Wikipedia paragraphs using a sequential encoder-decoder model

GRU

GRU (Gated Recurrent Unit) is a type of recurrent neural network (RNN) that improves the performance of RNN by introducing gates that control the flow of information
GRU's hidden state vector is updated in four steps
The reset gate determines how much of the previous hidden state is carried forward
The update gate determines how much of the new beliefs are blended into the current hidden state

Attention Mechanism

The attention mechanism in a Transformer network allows the model to selectively focus on different words in the input sequence
This mechanism allows the Transformer to understand the context better and avoid the problem of information loss
Attention heads in a Transformer can pick and choose how to combine information from nearby words, depending on the context

Model Training

The process of training a Transformer model typically includes pre-training on a general dataset of text and fine-tuning on a specific dataset for a particular task

Queries, Keys, Values

Queries, keys, and values are used in the attention mechanism
A query (for example, a word in a sentence) is compared to a key/value store (other words in the sentence)
The output is a sum of the values, weighted by the resonance between the query and each key.

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Description

This quiz covers the fundamentals of generative AI and focuses specifically on Generative Pre-trained Transformers (GPT). It explores the characteristics of large language models (LLMs), their architecture, and their applications. Perfect for anyone looking to deepen their understanding of AI technologies.