Podcast
Questions and Answers
Which of the following best describes the relationship between generative AI and traditional machine learning?
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?
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?
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?
What is the term used for the text input that is passed to a large language model?
What does the 'context window' refer to in the context of large language models?
What does the 'context window' refer to in the context of large language models?
What term describes the output generated by a large language model?
What term describes the output generated by a large language model?
What process is known as using the model to generate text?
What process is known as using the model to generate text?
Which architectural approach significantly enhanced the performance of natural language tasks and led to a surge in generative capability?
Which architectural approach significantly enhanced the performance of natural language tasks and led to a surge in generative capability?
What is the primary function of the attention mechanism in the transformer architecture?
What is the primary function of the attention mechanism in the transformer architecture?
Where are the attention weights established in a Language Model?
Where are the attention weights established in a Language Model?
What do attention maps help to visualize in the transformer model?
What do attention maps help to visualize in the transformer model?
What is the role of positional encoding in the transformer model?
What is the role of positional encoding in the transformer model?
What is the purpose of the multiple heads in multi-headed self-attention?
What is the purpose of the multiple heads in multi-headed self-attention?
After the attention weights have been applied, where are the outputs moved to next in the transformer model?
After the attention weights have been applied, where are the outputs moved to next in the transformer model?
What is the role of the softmax layer in the transformer architecture?
What is the role of the softmax layer in the transformer architecture?
What is 'prompt engineering' primarily focused on?
What is 'prompt engineering' primarily focused on?
Which type of model directly processes a prompt using the decoder's layers?
Which type of model directly processes a prompt using the decoder's layers?
What is the primary characteristic of in-context learning?
What is the primary characteristic of in-context learning?
What distinguishes zero-shot inference from other prompt strategies?
What distinguishes zero-shot inference from other prompt strategies?
What defines one-shot inference?
What defines one-shot inference?
When is few-shot inference most beneficial, according to the text?
When is few-shot inference most beneficial, according to the text?
In an encoder-decoder model, what is the role of the encoder with respect to the prompt?
In an encoder-decoder model, what is the role of the encoder with respect to the prompt?
What is the primary component the decoder in an encoder-decoder model uses to generate the final output?
What is the primary component the decoder in an encoder-decoder model uses to generate the final output?
Which of these sequences represents a progression from the least to most examples in a prompt?
Which of these sequences represents a progression from the least to most examples in a prompt?
What primarily limits the max tokens
parameter in a language model?
What primarily limits the max tokens
parameter in a language model?
What is the function of the max new tokens
parameter in a language model?
What is the function of the max new tokens
parameter in a language model?
If a language model uses greedy decoding, what strategy does it employ to select the next word?
If a language model uses greedy decoding, what strategy does it employ to select the next word?
What is the main purpose of random sampling in language model output generation?
What is the main purpose of random sampling in language model output generation?
What does the top-p
parameter control in a language model?
What does the top-p
parameter control in a language model?
How does increasing the top-p
value (closer to 1) typically affect the output of a language model?
How does increasing the top-p
value (closer to 1) typically affect the output of a language model?
What is the key difference between the configuration parameters of a generative model and its training parameters?
What is the key difference between the configuration parameters of a generative model and its training parameters?
Which of these is NOT a typical way to control the output of a generative language model?
Which of these is NOT a typical way to control the output of a generative language model?
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?
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?
What does setting top-p to 1.0 signify in the context of language model sampling?
What does setting top-p to 1.0 signify in the context of language model sampling?
How does lowering the top-p value generally affect the output of a language model?
How does lowering the top-p value generally affect the output of a language model?
What is the primary role of the temperature parameter in a language model?
What is the primary role of the temperature parameter in a language model?
How does a higher temperature parameter typically affect the generated text?
How does a higher temperature parameter typically affect the generated text?
What is the main purpose of Retrieval-Augmented Generation (RAG)?
What is the main purpose of Retrieval-Augmented Generation (RAG)?
According to the content, what is an advantage of RAG, compared to fine-tuning?
According to the content, what is an advantage of RAG, compared to fine-tuning?
Which scenario best illustrates the use of a high temperature parameter?
Which scenario best illustrates the use of a high temperature parameter?
What is the main goal of continuous pretraining of a language model?
What is the main goal of continuous pretraining of a language model?
Which type of fine-tuning focuses on adapting a model to follow user instructions more effectively?
Which type of fine-tuning focuses on adapting a model to follow user instructions more effectively?
What is the primary difference between fine-tuning and continuous pretraining?
What is the primary difference between fine-tuning and continuous pretraining?
What type of fine-tuning is exemplified by adapting a model for financial report summarization?
What type of fine-tuning is exemplified by adapting a model for financial report summarization?
What does fine-tuning primarily rely on to maximize the performance of a language model?
What does fine-tuning primarily rely on to maximize the performance of a language model?
Which of the following describes a drawback of continuous pretraining?
Which of the following describes a drawback of continuous pretraining?
What is the primary benefit of fine-tuning a large language model?
What is the primary benefit of fine-tuning a large language model?
How does domain adaptation fine-tuning enhance model performance?
How does domain adaptation fine-tuning enhance model performance?
Flashcards
Generative AI
Generative AI
A subset of traditional machine learning that generates content.
Large Language Models (LLMs)
Large Language Models (LLMs)
Machine learning models trained on vast amounts of text to understand and generate language.
Foundation Models
Foundation Models
Large models like GPT-3 and BERT, serving as the base for various AI tasks.
Prompt
Prompt
The input text given to a language model to initiate a task.
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Context Window
Context Window
The amount of text an LLM can consider for generating responses.
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Completion
Completion
The output generated by a language model in response to a prompt.
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Inference
Inference
The process of using a language model to generate text from a prompt.
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Transformer Architecture
Transformer Architecture
A model architecture improving natural language processing tasks, outperforming RNNs.
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Attention Weights
Attention Weights
Values learned that measure the importance of words in a sentence.
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Attention Map
Attention Map
A visual representation of attention weights between words.
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Encoder-Decoder
Encoder-Decoder
Two components of the transformer that work together.
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Embedding Layer
Embedding Layer
A trainable space where each word is represented as a vector.
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Multi-headed Self-attention
Multi-headed Self-attention
Multiple attention weights learned in parallel to capture different language aspects.
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Logits
Logits
Output from the feed-forward network representing probability scores for tokens.
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Prompt Engineering
Prompt Engineering
Development and improvement of prompts used to guide model responses.
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Few-shot learning
Few-shot learning
A method where multiple examples are included in a prompt to guide model output.
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Zero-shot prompts
Zero-shot prompts
Prompts that ask a model to produce an output without prior examples.
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One-shot inference
One-shot inference
A prompt that includes a single example to guide model behavior.
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In-context learning
In-context learning
A strategy where examples are included in the prompt to improve model performance.
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Encoder-Decoder Models
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
Decoder-Only Models
Models like GPT where the decoder processes both prompt and output.
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Zero-shot inference
Zero-shot inference
Classifying or generating outputs without providing specific examples in the prompt.
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Prompt Processing
Prompt Processing
How models interpret and respond to inputs to generate outputs.
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Generative Configuration
Generative Configuration
Parameters that influence a model's output during inference, different from training parameters.
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Max Tokens
Max Tokens
Sets the highest number of tokens processed in one attempt, including input and output.
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Max New Tokens
Max New Tokens
Limits the number of tokens the model generates during a response.
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Random Sampling
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
Greedy Decoding
A method where the model always selects the word with the highest probability.
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Top-P Sampling
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
Diversity vs. Coherence
The balance between producing creative outputs versus predictable, high-probability tokens.
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Top-p = 1.0
Top-p = 1.0
Allows all tokens to be considered without any filtering.
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Top-p < 1.0
Top-p < 1.0
Reduces choices to make outputs more predictable by limiting token selection.
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Temperature Parameter
Temperature Parameter
Controls randomness and creativity in language model outputs during token selection.
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Low Temperature
Low Temperature
Smoothing the probability distribution, encouraging more predictable outputs but less creative ones.
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High Temperature
High Temperature
Increases randomness, promoting more diverse and potentially nonsensical outputs.
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Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG)
An approach that enhances language model outputs by referencing external authoritative knowledge bases.
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RAG Benefits
RAG Benefits
Extends LLM capabilities without the need for retraining, optimizing responses for specific domains.
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Fine-Tuning
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
Task-Specific Fine-Tuning
Focuses on fine-tuning a model for a specific task like summarizing financial reports.
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Domain Adaptation
Domain Adaptation
Adapts a model to work better in specific fields like healthcare or law.
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Instruction Tuning
Instruction Tuning
Improves a model's ability to follow user instructions effectively.
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Continuous Pretraining
Continuous Pretraining
Extends a model’s training by exposing it to additional relevant text data.
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Supervised Learning in Fine-Tuning
Supervised Learning in Fine-Tuning
Uses labeled examples to improve the language model's output for specific tasks.
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Labeled Examples
Labeled Examples
Prompt-completion pairs used during fine-tuning to update a model's weights.
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Self-Supervised Learning
Self-Supervised Learning
A learning method using vast amounts of unstructured data without manual labeling.
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Web and Text Analytics 2024-25, Week 12
- Course material covers Web and Text Analytics
- Instructor is Evangelos Kalampokis
- Website: https://kalampokishub.io
- Lab website: http://islab.uom.gr
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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Description
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.