Summary of Towards Smaller, Faster Decoder-only Transformers: Architectural Variants and Their Implications, by Sathya Krishnan Suresh et al.
Towards smaller, faster decoder-only transformers: Architectural variants and their implications
by Sathya Krishnan Suresh, Shunmugapriya P
First submitted to arxiv on: 22 Apr 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 The paper introduces three modifications to the decoder-only transformer architecture, aiming to reduce Large Language Model (LLM) sizes while preserving their performance in language generation tasks. The proposed variants, ParallelGPT (pgpt), LinearGPT (lgpt), and ConvGPT (cgpt), demonstrate comparable results to the conventional architecture on language generation benchmarks, but with reduced model sizes and faster training times. The study’s findings are relevant to the development of more efficient and effective LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make Large Language Models better by changing how they’re built. Right now, these models use a special kind of computer architecture called the transformer. To make them work even better, researchers have been trying to add more transformers and train them on lots of data. But what if we could make these models smaller and faster to train without sacrificing their ability to generate language? That’s exactly what this study does. It creates three new versions of the transformer model that are all about being smaller and faster. These models, called ParallelGPT, LinearGPT, and ConvGPT, work just as well as the original model at generating text, but they’re more efficient. The researchers even share their code and the actual weights used in these models so other scientists can build upon this work. |
Keywords
» Artificial intelligence » Decoder » Large language model » Transformer
