Summary of Survey and Evaluation Of Converging Architecture in Llms Based on Footsteps Of Operations, by Seongho Kim et al.
Survey and Evaluation of Converging Architecture in LLMs based on Footsteps of Operations
by Seongho Kim, Jihyun Moon, Juntaek Oh, Insu Choi, Joon-Sung Yang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This research paper explores the evolution of Large Language Models (LLMs), driven by the Attention mechanism and Transformer architecture. As LLMs grew in size to accommodate more precise information, they demanded increasing storage and computational resources. To meet these demands, high-bandwidth memory and accelerators were developed, along with various model architectures. The study analyzes converged LLM architectures in terms of layer configurations, operational mechanisms, and model sizes, considering hyperparameter settings. A concise survey of LLM history is provided, tracing their operational improvements. Performance trends under different hyperparameters are summarized using the RTX 6000 Ada Lovelace architecture. Surprisingly, even identical models can exhibit varying behaviors depending on deployment environments or hyperparameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computer systems learn to understand and generate human-like text. It’s like a big puzzle where we’re trying to figure out what makes these language models work so well. We’ve seen huge improvements in the last few years, but that’s also made them much bigger and more complicated. To keep up with this growth, we need new ways of storing and processing information. The researchers looked at how different model designs perform when used for different tasks and on different machines. They found some interesting patterns and surprises along the way! |
Keywords
* Artificial intelligence * Attention * Hyperparameter * Transformer
