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.