Generative AI on Research Papers Using Nougat Model





Recent advances in large language models (LLMs) like GPT-4 have shown impressive capabilities in generating coherent text. However, parsing and understanding research papers accurately remains an extremely challenging task for AI. Research papers contain complex formatting, math equations, tables, figures, and domain-specific language. The density of information is very high and important semantics are encoded in the formatting.

In this article, I will demonstrate how a new model called Nougat from Meta can help parse research papers accurately. We then combine it with an LLM pipeline that extracts and summarizes all the tables in the paper.

The potential here is immense. There is a lot of data/information locked up in research papers and books that have not been parsed correctly. Accurate parsing enables their use in many different applications including LLM retraining.

I made a Youtube video explaining the code and my experiments in more detail. Check it out here.