LLM_for_Scientific_Research_Papers

Q: What are some of the most up to date LLM Tutorials online that will help developers build, train and create Large Language Models specifically for RAG for Scientific papers?

A: Building, training, and creating large language models (LLM) and Retrieval-Augmented Generation (RAG) models specifically for scientific papers is a specialized task. Currently, specific up-to-date tutorials dealing with this exact area may be limited. However, you can start with the following resources to learn about LLM, RAG, and their applications:

  1. Hugging Face's Transformers: This is a state-of-the-art Natural Language Processing library that contains implementations for many recent Large Language Models like GPT-3, BERT, and RoBERTa. Hugging Face also provides a great introduction to RAG:

  • Transformers library: https://huggingface.co/transformers/

  • RAG Introduction: https://huggingface.co/transformers/model_doc/rag.html

  1. Facebook's AI Research (FAIR): FAIR's publications on RAG contain extensive information about RAG theory and training, which will be beneficial if you plan to use RAG for scientific papers.

  • RAG Paper: https://arxiv.org/abs/2005.11401

  • BART Paper (a seq2seq model used in RAG): https://arxiv.org/abs/1910.13461

  1. Google's BERT: BERT is a popular transformer-based LLM originally presented by Google. The official BERT GitHub repository contains code and examples, which you can use as a starting point.

  • BERT GitHub: https://github.com/google-research/bert

  1. OpenAI's GPT-3: This is one of the largest language models currently existing. While direct tutorials may not be available, OpenAI provides an API and developer guide that you can use to build models.

  • GPT-3 Developer Guide: https://beta.openai.com/docs/guides/language/

  1. Coursera Course - DeepLearning.AI’s “Natural Language Processing Specialization”: This contains extensive information about transformer models and how to create them.

  • Course Link: https://www.coursera.org/specializations/natural-language-processing -Remember, when you build models for scientific papers with RAG, you have to fine-tune your models with a dataset of scientific papers to get a better performance.

B: Creating, training, and building Large Language Models specifically for Retriever Augmented Generation (RAG) might require a deep understanding of machine learning, natural language processing, and PyTorch.

  1. Hugging Face: The best place to start would be Hugging Face.Many of the best known language models come from their open-source library. They have a community and a large set of models to learn from.

    Tutorial: https://huggingface.co/transformers/notebooks.html

  2. PyTorch: Since the Hugging Face's transformers library is built on PyTorch, its tutorial can also be valuable, especially their NLP specific tutorials.

    Tutorial: https://pytorch.org/tutorials/index.html

  3. Coursera: Deep Learning Specialization from Coursera covers several aspects of deep learning and can be helpful.

    Tutorial: https://www.coursera.org/specializations/deep-learning

  4. Stanford University: cs224n: Natural Language Processing with Deep Learning is a standard course.

    Course info: http://web.stanford.edu/class/cs224n/

  5. NAACL 2021 Tutorial: Large Scale Knowledge Graph Based Synthetic Question Generation and Conversational Dialog Agents: Part 2.

    Tutorial: https://slideslive.com/38962784/large-scale-knowledge-graph-based-synthetic-question-generation-and-conversational-dialog-agents-part-2

Specific topics to dig into would be Transformers, BERT, GPT-3, and how these can be fine-tuned, as is common for RAG. Make sure to understand the Seq2Seq model, as it's the basis for much of the transformer advancement.

Please note that the field is advancing quickly, and the best source for the most recent advancements is often scientific papers, such as those found on arXiv.org. Especially those papers and tutorials related to Large Language Models and Question Answering Systems can be useful. It can also be good to follow leading researchers on medium or twitter as they often explain their papers there in a more digestible format.

Here are few relevant papers:

  • "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Patrick Lewis et.al

  • "Language Models are Few-Shot Learners" by Tom B. Brown et.al.

It is also worth noting that online communities, like Stack Overflow and the AI section of Reddit, might also provide useful resources and examples.

PreviousANH - CDS LLM Solution RoadmapNextLambard_Street

Last updated