Summary of Super Tiny Language Models, by Dylan Hillier et al.
Super Tiny Language Models
by Dylan Hillier, Leon Guertler, Cheston Tan, Palaash Agrawal, Chen Ruirui, Bobby Cheng
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Super Tiny Language Models (STLMs) that aim to deliver high performance with significantly reduced parameter counts. To achieve this, the authors explore innovative techniques such as byte-level tokenization with a pooling mechanism, weight tying, and efficient training strategies. These methods are designed to reduce the parameter count compared to traditional models. The paper will explore various subproblems, including tokenizer-free models, self-play based training, and alternative training objectives. The goal is to make high-performance language models more accessible and practical for a wide range of applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make really good computer programs that can understand human language better, but they use a lot of computing power and energy. To solve this problem, the researchers are working on small versions of these programs called Super Tiny Language Models (STLMs). They’re using new ideas like breaking down words into smaller pieces and training the models in special ways to make them work faster and use less computer power. The goal is to make these smart language programs more useful for lots of different things. |
Keywords
» Artificial intelligence » Tokenization » Tokenizer
