Course materials from the DeepLearning.ai Generative AI with LLMs course.

• 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.

• 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.

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

• 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)

• 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.

• 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.

• Scaling Instruction-Finetuned Language Models - Scaling fine-tuning with a focus on task, model size and chain-of-thought data.
• Introducing FLAN: More generalizable Language Models with Instruction Fine-Tuning - This blog (and article) explores instruction fine-tuning, which aims to make language models better at performing NLP tasks with zero-shot inference.

• HELM - Holistic Evaluation of Language Models - HELM is a living benchmark to evaluate Language Models more transparently.
• General Language Understanding Evaluation (GLUE) benchmark - This paper introduces GLUE, a benchmark for evaluating models on diverse natural language understanding (NLU) tasks and emphasizing the importance of improved general NLU systems.
• SuperGLUE - This paper introduces SuperGLUE, a benchmark designed to evaluate the performance of various NLP models on a range of challenging language understanding tasks.
• ROUGE: A Package for Automatic Evaluation of Summaries - This paper introduces and evaluates four different measures (ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S) in the ROUGE summarization evaluation package, which assess the quality of summaries by comparing them to ideal human-generated summaries.
• Measuring Massive Multitask Language Understanding (MMLU) - This paper presents a new test to measure multitask accuracy in text models, highlighting the need for substantial improvements in achieving expert-level accuracy and addressing lopsided performance and low accuracy on socially important subjects.
• BigBench-Hard - Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models - The paper introduces BIG-bench, a benchmark for evaluating language models on challenging tasks, providing insights on scale, calibration, and social bias.

• Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning - This paper provides a systematic overview of Parameter-Efficient Fine-tuning (PEFT) Methods in all three categories discussed in the lecture videos.
• On the Effectiveness of Parameter-Efficient Fine-Tuning - The paper analyzes sparse fine-tuning methods for pre-trained models in NLP.

• LoRA Low-Rank Adaptation of Large Language Models This paper proposes a parameter-efficient fine-tuning method that makes use of low-rank decomposition matrices to reduce the number of trainable parameters needed for fine-tuning language models.
• QLoRA: Efficient Finetuning of Quantized LLMs - This paper introduces an efficient method for fine-tuning large language models on a single GPU, based on quantization, achieving impressive results on benchmark tests.

• The Power of Scale for Parameter-Efficient Prompt Tuning - The paper explores "prompt tuning," a method for conditioning language models with learned soft prompts, achieving competitive performance compared to full fine-tuning and enabling model reuse for many tasks.

• 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.

• Training language models to follow instructions with human feedback - Paper by OpenAI introducing a human-in-the-loop process to create a model that is better at following instructions (InstructGPT).
• Learning to summarize from human feedback - This paper presents a method for improving language model-generated summaries using a reward-based approach, surpassing human reference summaries.

• Proximal Policy Optimization Algorithms - The paper from researchers at OpenAI that first proposed the PPO algorithm. The paper discusses the performance of the algorithm on a number of benchmark tasks including robotic locomotion and game play.
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model - This paper presents a simpler and effective method for precise control of large-scale unsupervised language models by aligning them with human preferences.

• Constitutional AI: Harmlessness from AI Feedback This paper introduces a method for training a harmless AI assistant without human labels, allowing better control of AI behavior with minimal human input.

• Chain-of-thought Prompting Elicits Reasoning in Large Language Models - Paper by researchers at Google exploring how chain-of-thought prompting improves the ability of LLMs to perform complex reasoning.
• PAL: Program-aided Language Models - This paper proposes an approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps.
• ReAct: Synergizing Reasoning and Acting in Language Models This paper presents an advanced prompting technique that allows an LLM to make decisions about how to interact with external applications.

• LangChain Library (GitHub) This library is aimed at assisting in the development of those types of applications, such as Question Answering, Chatbots and other Agents. You can read the documentation here.
• Who Owns the Generative AI Platform? The article examines the market dynamics and business models of generative AI.