Summary of Asterisk*: Keep It Simple, by Andrew Semenov
Asterisk*: Keep it Simple
by Andrew Semenov
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 paper introduces Asterisk, a compact GPT-based model generating text embeddings. The minimalist architecture features two layers, two attention heads, and 256 embedding dimensions. By distilling knowledge from larger pretrained models, the authors investigate trade-offs between model size and performance while minimizing computational and memory requirements. Experimentally, Asterisk achieves moderate performance in zero-shot classification across various downstream applications. With additional configuration, its performance can approach or surpass that of larger architectures on specific classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Asterisk is a new way to create text embeddings using a smaller GPT model. The researchers designed this model to be simple and efficient while still being good at classifying text. They tested Asterisk on many different tasks and found it worked okay, but not as well as bigger models. By making some adjustments, they were able to get Asterisk to perform almost as well as the larger models on certain tasks. |
Keywords
» Artificial intelligence » Attention » Classification » Embedding » Gpt » Zero shot
