Chapter 2: Understanding Foundation Models

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Questions and Answers

What is a foundation model?

A foundation model is a type of machine learning model that can be used to build applications.

Which of the following is NOT a common design decision for foundation models?

  • Training data
  • Model architecture
  • Model size
  • Number of GPUs used (correct)

Transformer architecture is the only architecture used in language-based foundation models.

False (B)

What are the two steps involved in the pre-training process of a foundation model?

<p>Pre-training is often divided into two steps: pre-training and post-training. Pre-training makes a model capable, but not necessarily safe or easy to use. Post-training is where you align the model with human preferences.</p> Signup and view all the answers

What is the difference between parameters and hyperparameters in a model?

<p>Parameters are learned by the model during training, while hyperparameters are set by users to control how the model learns.</p> Signup and view all the answers

The scaling law states that the number of training tokens should be 20 times the model size for optimal performance.

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

What are the two main types of post-training?

<p>Post-training is generally divided into two steps: Supervised Fine-tuning (SFT) and Preference Finetuning. SFT focuses on making the model better at understanding instructions and performing tasks, while Preference Finetuning focuses on aligning the model with human preferences.</p> Signup and view all the answers

How does the "best of N" method work for test time compute?

<p>The &quot;best of N&quot; method involves generating multiple outputs from the model and then selecting the output that performs best based on a defined metric.</p> Signup and view all the answers

Hallucinations are a major obstacle in training large language models but have no real-world impact when the model is deployed.

<p>False (B)</p> Signup and view all the answers

What is the primary reason for the internet data bottleneck in the training of large language models?

<p>The increasing rate of data generation (C)</p> Signup and view all the answers

What is the most common category of tasks that require structured outputs?

<p>Semantic parsing, which involves converting natural language to a structured, machine-readable format.</p> Signup and view all the answers

What is the purpose of constrained sampling?

<p>Constrained sampling guides the model's generation process to ensure that the generated outputs adhere to specific format constraints.</p> Signup and view all the answers

Finetuning is the most effective and general approach to ensure that models generate structured outputs.

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

The probabilistic nature of large language models is always a positive factor for their performance and reliability.

<p>False (B)</p> Signup and view all the answers

What are the two main scenarios that demonstrate model inconsistency?

<p>Model inconsistency can manifest in two ways: (1) same input, different outputs - where identical prompts produce differing responses, and (2) slightly different input, drastically different outputs - where minor changes in the prompt can result in significantly varied responses.</p> Signup and view all the answers

What two potential approaches can help mitigate hallucinations in language models?

<p>Two primary approaches are: (1) incorporating factual and counterfactual signals in training data to encourage the model to rely on verified information, and (2) refining the model to provide more accurate information and flag uncertainties by prompting the model to say &quot;I don't know&quot; when necessary.</p> Signup and view all the answers

Flashcards

Post-Training

The process of adjusting a pre-trained model to produce outputs that align with human preferences.

Supervised Finetuning (SFT)

A process that uses high-quality instruction data to fine-tune a pre-trained model for conversational tasks.

Self-Supervised Pre-training

A type of machine learning where a model learns to predict the next token in a sequence based on previous tokens.

Reward Model (RM)

A key component of RLHF, it's a model trained to assess the quality of generated responses.

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Reinforcement Learning from Human Feedback (RLHF)

A method of training a model to generate outputs that maximize scores from a reward model.

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Demonstration Data

A collection of (prompt, response) pairs used to train a model to understand various requests and generate appropriate responses.

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Model Capacity

The ability of a model to learn from its training data and generate responses that fit the context of the input.

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Model Size

The size of a model, measured by the number of parameters it has.

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Domain Specificity

A measure of how well a model can handle tasks in specific areas like coding, medicine, or law.

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General-Purpose Model

A model designed to perform well on a wide range of tasks and domains.

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Tokenization

The process of breaking down text into smaller units, called tokens.

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Training Data

Data used for training a model, often collected from various sources like websites, blogs, and books.

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Language Distribution

The distribution of languages present in a training dataset.

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Low-Resource Languages

Languages that are under-represented in training datasets.

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Model Architecture

The architecture of a model, the underlying structure that defines its computational process.

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Transformer Architecture

An architecture that relies on the attention mechanism, handling sequences of text more efficiently than previous models.

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Attention Mechanism

A mechanism that enables a model to focus on specific parts of the input sequence during processing.

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Transformer Block

A building block of the transformer architecture, containing attention and feed-forward layers.

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Recurrent Neural Network (RNN)

A type of neural network that processes input sequentially, considering previous inputs to generate the next output.

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Sequence-to-Sequence (seq2seq) Architecture

A neural network architecture that uses an encoder to process inputs and a decoder to generate outputs.

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Activation Functions

Non-linear functions used in neural networks to allow the model to learn complex patterns.

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Mixture-of-Experts (MoE)

A type of sparse model that divides its parameters into groups, each specialized in a certain aspect.

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FLOPS

A measure of the compute needed to train a model, expressed in floating point operations.

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Chinchilla Scaling Law

A rule that helps determine the optimal model size and dataset size for a given compute budget.

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Generalization

The ability of a model to perform well on unseen tasks that it has never encountered during training.

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Emergent Abilities

A phenomenon where a model suddenly gains new abilities as its size and training data increase.

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Scaling Extrapolation

The practice of predicting the optimal hyperparameters for large models based on observations from smaller models.

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Data Bias

The tendency of a model to generate outputs that are consistent with the overall distribution of its training data but might not always be accurate or relevant.

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Output Ranking

The process of using a model to generate multiple outputs and selecting the best one based on specific criteria.

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Data Synthesis

A process for generating artificial data that mimics real-world data.

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Text Completion

The process of using a model to complete a sequence by predicting the next token.

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Study Notes

Chapter 2: Understanding Foundation Models

  • Foundation models are necessary to build applications using them
  • High-level understanding of models helps users choose and adapt
  • Model training is complex and costly, rarely publicly disclosed due to confidentiality
  • Downstream applications are impacted by design choices in foundation models
  • Training data, model architecture and size, and post-training alignment with human preferences differ between foundation models
  • Models learn from data, their training data reveal capabilities and limitations
  • Model developers curate training data, focusing on data distribution
  • Chapter 8 explores dataset engineering and techniques (data quality evaluation, data synthesis) in detail
  • Transformer architecture is the dominant architecture today
    • Transformer model size is a frequent concern from model users
    • Model developer determine appropriate size using methods from the chapter
  • Model training is often split into pre-training and post-training stages
    • Pre-training makes models capable, but not necessarily usable
    • Post-training aims to align the model with human preferences
  • Model performance impacted by how models are trained, rather than just the training itself
  • The impact of sampling on model performance is often overlooked, sampling is how models choose an output
  • Concepts covered include training, sampling, and important considerations for deep learning model usage
  • Curated datasets for different domains and languages is an important consideration when building a successful model.
  • English-language content heavily dominates internet data, while other languages may not have sufficient representation
  • Using heuristics to filter data from the internet is used by some teams, for example OpenAl using Reddit votes to train GPT-2
  • Models are sometimes better at tasks present in the training data than those not present
  • Models that are trained well on high-quality data may perform better than those trained on large quantities of poor-quality data

Training Data

  • Al model quality is directly proportional to the data it was trained on
  • If the model lacks data, it won't perform well on the given tasks
  • Using more, or better, training data improves a models capability in a given task
  • Common Crawl is a source for training data on the internet
  • This data collection method and related information was crawled over 2-3 billion web pages during 2022-2023
  • Data quality of resources like Common Crawl is questionable and might contain misinformation, propaganda, conspiracy, or other erroneous content
  • Common Crawl and variations continue to be used in many foundation models
  • Model developers often take available data, even when it doesn't align perfectly with their needs
  • Variations of Common Crawl are frequently used by companies, such as OpenAl and Google's

Multilingual Models

  • English content heavily dominates the internet
  • Almost half of Common Crawl is English-language content
  • English language models are much more prevalent and perform better than underrepresented and low-resource languages

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Understanding Foundation Models in AI Engineering PDF

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