Generative AI and Language Models Quiz
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Questions and Answers

Which of the following best describes the relationship between generative AI and traditional machine learning?

  • Generative AI is a type of traditional machine learning. (correct)
  • Generative AI is a completely separate field from traditional machine learning.
  • Generative AI and traditional machine learning are independent with no overlap.
  • Traditional machine learning is a subset of generative AI.

What is the primary way large language models learn their abilities?

  • By finding statistical patterns in massive datasets of human-generated content. (correct)
  • By interacting with the physical world and adapting their language through experience.
  • Through a process of manual annotation and fine-tuning by human experts.
  • By being directly programmed with specific rules for language.

Which of these models has the largest number of parameters, according to the provided information?

  • BERT
  • LLaMA
  • PaLM (correct)
  • GPT-3

What is the term used for the text input that is passed to a large language model?

<p>Prompt (A)</p> Signup and view all the answers

What does the 'context window' refer to in the context of large language models?

<p>The space or memory available to the prompt. (D)</p> Signup and view all the answers

What term describes the output generated by a large language model?

<p>Completion (C)</p> Signup and view all the answers

What process is known as using the model to generate text?

<p>Inference (D)</p> Signup and view all the answers

Which architectural approach significantly enhanced the performance of natural language tasks and led to a surge in generative capability?

<p>Transformer architecture (C)</p> Signup and view all the answers

What is the primary function of the attention mechanism in the transformer architecture?

<p>To learn the importance of each word in a sequence relative to all other words. (D)</p> Signup and view all the answers

Where are the attention weights established in a Language Model?

<p>During the training of the language model. (A)</p> Signup and view all the answers

What do attention maps help to visualize in the transformer model?

<p>The relationship between tokens. (A)</p> Signup and view all the answers

What is the role of positional encoding in the transformer model?

<p>To provide data about the location of each token in the sequence (B)</p> Signup and view all the answers

What is the purpose of the multiple heads in multi-headed self-attention?

<p>To allow the model to learn different aspects of language in parallel. (D)</p> Signup and view all the answers

After the attention weights have been applied, where are the outputs moved to next in the transformer model?

<p>To a fully-connected feed-forward network. (D)</p> Signup and view all the answers

What is the role of the softmax layer in the transformer architecture?

<p>To transform the logits into probability scores. (B)</p> Signup and view all the answers

What is 'prompt engineering' primarily focused on?

<p>Creating effective instructions for the model in the prompt. (C)</p> Signup and view all the answers

Which type of model directly processes a prompt using the decoder's layers?

<p>Decoder-only models (D)</p> Signup and view all the answers

What is the primary characteristic of in-context learning?

<p>It includes task examples directly in the prompt. (B)</p> Signup and view all the answers

What distinguishes zero-shot inference from other prompt strategies?

<p>It does not include any examples in the prompt. (C)</p> Signup and view all the answers

What defines one-shot inference?

<p>A single example is included in the prompt. (A)</p> Signup and view all the answers

When is few-shot inference most beneficial, according to the text?

<p>When used with smaller models. (B)</p> Signup and view all the answers

In an encoder-decoder model, what is the role of the encoder with respect to the prompt?

<p>It processes the prompt to create a contextual representation. (B)</p> Signup and view all the answers

What is the primary component the decoder in an encoder-decoder model uses to generate the final output?

<p>The contextual representation provided by encoder. (A)</p> Signup and view all the answers

Which of these sequences represents a progression from the least to most examples in a prompt?

<p>Zero-shot, one-shot, few-shot (B)</p> Signup and view all the answers

What primarily limits the max tokens parameter in a language model?

<p>The context window size. (C)</p> Signup and view all the answers

What is the function of the max new tokens parameter in a language model?

<p>To control the maximum amount of tokens a model can produce, but not a guarantee of that exact number. (C)</p> Signup and view all the answers

If a language model uses greedy decoding, what strategy does it employ to select the next word?

<p>It always selects the word with the highest probability. (C)</p> Signup and view all the answers

What is the main purpose of random sampling in language model output generation?

<p>To introduce variability in the generated text. (A)</p> Signup and view all the answers

What does the top-p parameter control in a language model?

<p>The subset of possible next tokens the model considers when generating text. (C)</p> Signup and view all the answers

How does increasing the top-p value (closer to 1) typically affect the output of a language model?

<p>It makes the output more diverse and creative. (C)</p> Signup and view all the answers

What is the key difference between the configuration parameters of a generative model and its training parameters?

<p>Configuration parameters influence the model's output during inference, while training parameters are learned during training time. (D)</p> Signup and view all the answers

Which of these is NOT a typical way to control the output of a generative language model?

<p>Adjusting the context window size. (D)</p> Signup and view all the answers

In top-p sampling, if token probabilities are: mat=0.4, floor=0.3, roof=0.15, sofa=0.1, tree=0.05, and top-p is set to 0.7, which tokens will the model consider?

<p>mat, floor (C)</p> Signup and view all the answers

What does setting top-p to 1.0 signify in the context of language model sampling?

<p>It considers all tokens, with no filtering applied. (B)</p> Signup and view all the answers

How does lowering the top-p value generally affect the output of a language model?

<p>It narrows down the choices, leading to more predictable text. (A)</p> Signup and view all the answers

What is the primary role of the temperature parameter in a language model?

<p>It controls the randomness or creativity of the generated output. (A)</p> Signup and view all the answers

How does a higher temperature parameter typically affect the generated text?

<p>It tends to generate more creative and diverse outputs but might be nonsensical sometimes. (B)</p> Signup and view all the answers

What is the main purpose of Retrieval-Augmented Generation (RAG)?

<p>To enable a language model to reference an external knowledge base before generating output. (B)</p> Signup and view all the answers

According to the content, what is an advantage of RAG, compared to fine-tuning?

<p>It allows the model to access specific domain knowledge without retraining. (C)</p> Signup and view all the answers

Which scenario best illustrates the use of a high temperature parameter?

<p>Generating creative fiction with unexpected phrases. (B)</p> Signup and view all the answers

What is the main goal of continuous pretraining of a language model?

<p>To enhance the model's foundational knowledge in a specific domain (A)</p> Signup and view all the answers

Which type of fine-tuning focuses on adapting a model to follow user instructions more effectively?

<p>Instruction Tuning (D)</p> Signup and view all the answers

What is the primary difference between fine-tuning and continuous pretraining?

<p>Fine-tuning utilizes labeled data while continuous pretraining uses unlabeled data (D)</p> Signup and view all the answers

What type of fine-tuning is exemplified by adapting a model for financial report summarization?

<p>Task-Specific Fine-Tuning (A)</p> Signup and view all the answers

What does fine-tuning primarily rely on to maximize the performance of a language model?

<p>A dataset of labeled prompt-completion pairs (D)</p> Signup and view all the answers

Which of the following describes a drawback of continuous pretraining?

<p>It requires large amounts of domain-specific data (B)</p> Signup and view all the answers

What is the primary benefit of fine-tuning a large language model?

<p>Enhances specific task performance using labeled data (A)</p> Signup and view all the answers

How does domain adaptation fine-tuning enhance model performance?

<p>By tailoring the model for specific types of content (B)</p> Signup and view all the answers

Flashcards

Generative AI

A subset of traditional machine learning that generates content.

Large Language Models (LLMs)

Machine learning models trained on vast amounts of text to understand and generate language.

Foundation Models

Large models like GPT-3 and BERT, serving as the base for various AI tasks.

Prompt

The input text given to a language model to initiate a task.

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Context Window

The amount of text an LLM can consider for generating responses.

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Completion

The output generated by a language model in response to a prompt.

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Inference

The process of using a language model to generate text from a prompt.

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Transformer Architecture

A model architecture improving natural language processing tasks, outperforming RNNs.

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Attention Weights

Values learned that measure the importance of words in a sentence.

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Attention Map

A visual representation of attention weights between words.

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Encoder-Decoder

Two components of the transformer that work together.

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Embedding Layer

A trainable space where each word is represented as a vector.

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Multi-headed Self-attention

Multiple attention weights learned in parallel to capture different language aspects.

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Logits

Output from the feed-forward network representing probability scores for tokens.

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Prompt Engineering

Development and improvement of prompts used to guide model responses.

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Few-shot learning

A method where multiple examples are included in a prompt to guide model output.

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Zero-shot prompts

Prompts that ask a model to produce an output without prior examples.

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One-shot inference

A prompt that includes a single example to guide model behavior.

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In-context learning

A strategy where examples are included in the prompt to improve model performance.

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Encoder-Decoder Models

Models like T5 that process input with an encoder and generate output with a decoder.

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Decoder-Only Models

Models like GPT where the decoder processes both prompt and output.

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Zero-shot inference

Classifying or generating outputs without providing specific examples in the prompt.

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Prompt Processing

How models interpret and respond to inputs to generate outputs.

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Generative Configuration

Parameters that influence a model's output during inference, different from training parameters.

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Max Tokens

Sets the highest number of tokens processed in one attempt, including input and output.

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Max New Tokens

Limits the number of tokens the model generates during a response.

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Random Sampling

A technique where the model selects words randomly based on their probability distribution instead of always the highest.

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Greedy Decoding

A method where the model always selects the word with the highest probability.

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Top-P Sampling

Allows selection from a subset of next tokens, where the cumulative probability meets a threshold.

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Diversity vs. Coherence

The balance between producing creative outputs versus predictable, high-probability tokens.

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Top-p = 1.0

Allows all tokens to be considered without any filtering.

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Top-p < 1.0

Reduces choices to make outputs more predictable by limiting token selection.

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Temperature Parameter

Controls randomness and creativity in language model outputs during token selection.

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Low Temperature

Smoothing the probability distribution, encouraging more predictable outputs but less creative ones.

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High Temperature

Increases randomness, promoting more diverse and potentially nonsensical outputs.

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Retrieval-Augmented Generation (RAG)

An approach that enhances language model outputs by referencing external authoritative knowledge bases.

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RAG Benefits

Extends LLM capabilities without the need for retraining, optimizing responses for specific domains.

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Fine-Tuning

The process of adapting a pre-trained language model to specific tasks by continuing its training.

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Task-Specific Fine-Tuning

Focuses on fine-tuning a model for a specific task like summarizing financial reports.

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Domain Adaptation

Adapts a model to work better in specific fields like healthcare or law.

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Instruction Tuning

Improves a model's ability to follow user instructions effectively.

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Continuous Pretraining

Extends a model’s training by exposing it to additional relevant text data.

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Supervised Learning in Fine-Tuning

Uses labeled examples to improve the language model's output for specific tasks.

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Labeled Examples

Prompt-completion pairs used during fine-tuning to update a model's weights.

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Self-Supervised Learning

A learning method using vast amounts of unstructured data without manual labeling.

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Study Notes

Web and Text Analytics 2024-25, Week 12

Sam Altman's Reflections for 2024

  • Altman discusses the potential of superintelligence
  • Current AI products are appreciated, but focus is on future superintelligence
  • Superintelligent tools can greatly accelerate scientific discovery and innovation
  • Abundance and prosperity are projected to increase significantly
  • The transition to superintelligence is considered a leap comparable to past innovations
  • Important to act responsibly, maximizing broad benefit and empowerment
  • OpenAI's path is not aligned with a typical company model given its potential

Large Language Models

  • Generative AI is a subset of traditional machine learning
  • Generative AI models learn patterns from massive datasets of human-generated content
  • Large Language Models (LLMs) are trained on trillions of words and use significant computing power
  • Various foundational models like GPT-3, BERT, T5, PaLM, Llama 3.5, Claude 3.5 exist with different parameter counts

LLM - Terminology

  • LLMs interact differently than traditional machine learning (ML).
  • LLMs use natural language prompts, not code and APIs.
  • Prompts are the text input provided to LLMs.
  • The prompt's context window holds input data; its size varies by model.
  • Model's text output is called completion, producing text via inference

Transformer Architecture

  • Transformer architecture greatly boosts natural language tasks compared to RNNs
  • This architecture's strength is learning relationships among words in a sentence
  • Attention weights show links between words during LLM training
  • Attention maps show the connection between words

Encoder-Decoder

  • Transformers consist of encoders and decoders
  • They work together, sharing characteristics
  • The embedding layer creates a vector representation for each token in a high-dimensional space.

Tokenizer – Embedding – Positional Encoding

  • Tokenizers break down input text into tokens
  • Embeddings convert tokens to vectors
  • Using positional encoding, the position of each token in the sequence is encoded to represent the token's context.

Multi-headed Self-Attention

  • Input tokens and positional encodings are processed through a self-attention layer.
  • Self-attention weights reflect the importance of each word to other words in the input sequence.
  • Multiple attention heads learn different aspects of language in parallel during training
  • Multi-headed self-attention is part of the transformer's architecture

Feed-Forward Network

  • Attention weights feed into a fully connected network
  • The network produces a vector (logits) of probabilities for each token, based on the input context and training data.
  • The logits are normalized by a softmax layer to get the probability score

Prompt Engineering

  • This involves developing and improving prompts to optimize LLM outputs.
  • Prompt types include instruction-based, few-shot, and zero-shot prompts

Transformer Layers and the Prompt

  • Encoder-Decoder models (like T5): the encoder processes input text to create a representation, and the decoder uses that representation & its own self-attention mechanism for output.
  • Decoder-Only models (like GPT): the prompt is directly processed by the decoder layers

In-context Learning

  • Examples within the prompt help LLMs generate better outcomes
  • One example is called one-shot inference, multiple examples are few-shot inference

Zero-Shot Inference

  • The prompt provides tasks, the context, and the type of output desired
  • This method does not utilize pre-provided examples

Zero-Shot Evaluation

  • This tests the performance of models without examples

One-Shot Inference

  • The prompt contains a sample input and expected output to demonstrate context

Few-Shot Inference

  • Several examples are given within the prompt demonstrating the task and desired output

Continuous Pretraining Vs Fine-Tuning

  • Continuous pretraining improves overall general language understanding through exposure to additional data
  • Fine-tuning focuses on a particular task or set of tasks via labeled sample prompts and outputs.

Fine-tuning LLMs

  • Fine-tuning adjusts a pre-trained model for specialized tasks and leverages initial language understanding
  • Different approaches include task-specific fine-tuning for specialized tasks, domain adaptation for different domains, instruction tuning for effective instructions.

Parameter-Efficient Fine-Tuning (PEFT)

  • Parameter-efficient methods update a subset of parameters to avoid catastrophic forgetting and improve efficiency.
  • Techniques may involve freezing some parts of the model and training only specific sections

Max Tokens

  • Input and output tokens are simultaneously limited by the max token setting.
  • The max tokens parameter is connected to the context window.

Max new tokens

  • Max new tokens limit the number of generated tokens

Random Sampling

  • LLMs can use random sampling in the output process to introduce variability to reduce bias
  • Alternative to greedy decoding

Top-P

  • Top-P sampling selects tokens based on the cumulative probability; higher Top-P values will result in more diverse outputs

Temperature

  • Temperature controls output randomness.
  • Lower temperatures produce more predictable outputs, while higher temperatures allow more diverse outputs

Retrieval Augmented Generation (RAG)

  • RAG enhances LLM response quality by incorporating external knowledge sources.
  • Reduces the need for retraining the model for specific use cases.

Fine-tuning On a Single Task

  • Fine-tuning can be used on a single task in contrast to continuous pretraining
  • This may cause catastrophic forgetting.
  • PEFT may help this problem

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Test your knowledge on the intricacies of generative AI and large language models. This quiz covers essential concepts such as the relationship between generative and traditional machine learning, the architecture of transformers, and the function of attention mechanisms. Dive into the world of AI and enhance your understanding of how these technologies operate.

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