How to build a Large Language Model to understand Scientific papers!

Building a Large Language Model (LLM) to understand scientific papers involves several steps and considerations.

1. Data Gathering: Data is the most crucial component in building any machine learning model. For scientific papers, you can use databases like PubMed, arXiv, JSTOR, among others. These databases contain millions of scientific papers across various disciplines. Make sure to follow the copyright and privacy requirements of these databases.

2. Data Preparation: Scientific papers are mostly in PDF format, you need to convert them to a format suitable for the model training (like .txt). You might also need to clean the text (removing references, tables, etc.) and normalize it (lowercasing, stemming, etc.).

3. Tokenization: This step involves converting the text into a format (tokens) that the machine learning model can understand.

4. Model Selection: When it comes to language models, there are several pre-existing architectures you can use such as Transformer models like GPT-3, BERT, RoBERTa, etc. Pre-trained models can provide a good starting point because they already understand the basic structure of language and can be fine-tuned for specific tasks.

5. Training: Feed the tokenized text into the model. You might need a lot of computational resources when training large language models. And also, this process can take a lot of time, possibly several weeks or months.

6. Evaluation & Tuning: After the model has been trained, evaluate its performance using a validation set. This will give you an indication of how well your model is doing. Based on the performance, you might need to tune your model or re-consider some of your previous steps.

7. Implementation & Usage: After you're satisfied with the model's performance, you can start using it to understand new scientific documents.

These are the general steps involved, but remember building a large language model is a complex task that requires deep knowledge in Natural Language Processing (NLP), machine learning, and computational linguistics.

Resources:

1. Tooling for scientific paper databases - PubMed (https://pubmed.ncbi.nlm.nih.gov/), arXiv (https://arxiv.org/), JSTOR (https://www.jstor.org/)

2. Tokenization Techniques - https://towardsdatascience.com/tokenization-5bcbd15b8b5

3. Pretrained models and libraries - Huggingface Transformers (https://huggingface.co/)

4. Detailed guide for language model - https://jalammar.github.io/illustrated-gpt2/ and Stanford's Hing Face presentation on Language Models (https://www.youtube.com/watch?v=SysgYptB198)

5. Training guide - Deep Learning Specialization by Andrew NG on Coursera (https://www.coursera.org/specializations/deep-learning)