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I asked ChatGPT to explain this text from the generative-ai class:

Give me references I can read to understand the following better - Arxiv links, youtube videos, lectures notes, and blog posts etc. — Remember that the output of an LLM is a probability distribution across tokens. So you can compare the distribution of the completion and that of the training label and use the standard crossentropy function to calculate loss between the two token distributions. And then use the calculated loss to update your model weights in standard backpropagation. You’ll do this for many batches of prompt completion pairs and over several epochs, update the weights so that the model’s performance on the task improves. As in standard supervised learning, you can define separate evaluation steps to measure your LLM performance using the holdout validation data set. This will give you the validation accuracy, and after you’ve completed your fine tuning, you can perform a final performance evaluation using the holdout test data set. This will give you the test accuracy. The fine-tuning process results in a new version of the base model, often called an instruct model that is better at the tasks you are

The answer was:

You’re asking about the process of training and fine-tuning large language models (LLMs) using methods like cross-entropy loss, backpropagation, and evaluation using holdout validation and test data sets. Below are references that should help you gain a deeper understanding of each aspect:

Understanding Language Models & NLP Basics

1. YouTube: Stanford’s Natural Language Processing with Deep Learning (CS224N) course offers a good introduction to language models. Stanford CS224N
2. Arxiv: “A Gentle Introduction to Transformers” - This is not a paper, but a simplified explanation to get you up to speed. Link

Cross-Entropy Loss & Backpropagation

1. Blog Post: “Understanding Cross-Entropy Loss in Machine Learning” Link
2. YouTube: 3Blue1Brown’s “What is backpropagation really doing?” for a visual and intuitive understanding of backpropagation. 3Blue1Brown Video

Batch Training and Epochs

1. Blog Post: “What is a Batch in Deep Learning?” Link
2. Lecture Notes: “Efficient Mini-batch Training” by UBC Link

Evaluation Metrics

1. Blog Post: “Understanding Data Splitting for Machine Learning” explains the importance of splitting data into training, validation, and test sets. Link
2. Arxiv: “A Reliable Effective Terascale Linear Learning System” provides insights into evaluation methods. Link

Fine-tuning LLMs

1. Arxiv: “How to Fine-Tune BERT for Text Classification?” This paper discusses techniques for fine-tuning models like BERT, which can be generalized to other large language models. Link
2. Blog Post: Hugging Face’s “A Step-by-Step Guide to Fine-tuning BERT” is another practical guide. Link

General Overview and Best Practices

1. Arxiv: “Best Practices for Fine-Tuning Transformers on Downstream Tasks” provides an overview of the best methods for fine-tuning. Link

These resources should give you a well-rounded understanding of the topics you’re interested in. Happy learning!