Summary of From Text to Transformation: a Comprehensive Review Of Large Language Models’ Versatility, by Pravneet Kaur et al.
From Text to Transformation: A Comprehensive Review of Large Language Models’ Versatility
by Pravneet Kaur, Gautam Siddharth Kashyap, Ankit Kumar, Md Tabrez Nafis, Sandeep Kumar, Vikrant Shokeen
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This groundbreaking study delves into the vast capabilities of Large Language Models (LLMs), such as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), across diverse domains like technology, finance, healthcare, education, fitness, and holistic well-being. The review paper provides a comprehensive analysis of LLMs’ utility in various sectors, highlighting research gaps and untapped potential for applications in areas such as urban planning, climate modelling, and disaster response. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Large Language Models (LLMs) can help us. It talks about how these models are used in different areas like fitness, wellness, city planning, predicting the weather, and helping with natural disasters. The researchers wanted to see where LLMs can be used better and what we still need to learn. |
Keywords
» Artificial intelligence » Bert » Encoder » Gpt » Transformer
