Extra resources

Below you'll find links to the research papers discussed in this weeks videos. You don't need to understand all the technical details discussed in these papers - you have already seen the most important points you'll need to answer the quizzes in the lecture videos.

However, if you'd like to take a closer look at the original research, you can read the papers and articles via the links below.

Generative AI Lifecycle

• Generative AI on AWS: Building Context-Aware, Multimodal Reasoning Applications - This O'Reilly book dives deep into all phases of the generative AI lifecycle including model selection, fine-tuning, adapting, evaluation, deployment, and runtime optimizations.

Transformer Architecture

• Attention is All You Need - This paper introduced the Transformer architecture, with the core "self-attention" mechanism. This article was the foundation for LLMs.

• BLOOM: BigScience 176B Model - BLOOM is a open-source LLM with 176B parameters trained in an open and transparent way. In this paper, the authors present a detailed discussion of the dataset and process used to train the model. You can also see a high-level overview of the model here.

• Vector Space Models - Series of lessons from DeepLearning.AI's Natural Language Processing specialization discussing the basics of vector space models and their use in language modeling.

Pre-training and scaling laws

• Scaling Laws for Neural Language Models - empirical study by researchers at OpenAI exploring the scaling laws for large language models.

Model architectures and pre-training objectives

• What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? - The paper examines modeling choices in large pre-trained language models and identifies the optimal approach for zero-shot generalization.

• HuggingFace Tasks and Model Hub - Collection of resources to tackle varying machine learning tasks using the HuggingFace library.

• LLaMA: Open and Efficient Foundation Language Models - Article from Meta AI proposing Efficient LLMs (their model with 13B parameters outperform GPT3 with 175B parameters on most benchmarks)

Scaling laws and compute-optimal models

• Language Models are Few-Shot Learners - This paper investigates the potential of few-shot learning in Large Language Models.

• Training Compute-Optimal Large Language Models - Study from DeepMind to evaluate the optimal model size and number of tokens for training LLMs. Also known as "Chinchilla Paper".

• BloombergGPT: A Large Language Model for Finance - LLM trained specifically for the finance domain, a good example that tried to follow chinchilla laws.