Summary of Survey Of Different Large Language Model Architectures: Trends, Benchmarks, and Challenges, by Minghao Shao et al.
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges
by Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers provide a comprehensive overview of recent advancements in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). These AI models have become increasingly complex, with dozens of neural network layers and billions to trillions of parameters. LLMs are trained on vast datasets using transformer block architectures, enabling them to perform tasks such as text generation, language translation, question answering, code generation, and analysis. MLLMs extend these capabilities by processing multiple data modalities like images, audio, and video, allowing for applications like video editing, image comprehension, and captioning. The paper explores the evolution of LLMs, the nuances of MLLMs, and analyzes state-of-the-art models, discussing their features, strengths, limitations, challenges, and future prospects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a type of artificial intelligence called Large Language Models (LLMs). These AI models are very good at understanding and generating human language. They have many layers and a lot of information stored in them. LLMs can do things like generate text, translate languages, answer questions, write code, and understand other types of data. Some LLMs can even process images, audio, and video. This paper looks back on how LLMs evolved and then talks about the special ones that can handle multiple types of data. It also analyzes some of the best LLMs right now, discussing what makes them good or bad. |
Keywords
» Artificial intelligence » Neural network » Question answering » Text generation » Transformer » Translation
