Summary of Mechanistic Interpretability Of Binary and Ternary Transformers, by Jason Li
Mechanistic Interpretability of Binary and Ternary Transformers
by Jason Li
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 explores the potential of reducing memory usage and improving inference speed in Large Language Models (LLMs) by applying transformer networks with reduced precision, specifically binary and ternary networks. The study builds upon previous work (arXiv:2310.11453, arXiv:2402.17764) and investigates whether these networks learn distinct or similar algorithms compared to full-precision transformer networks. The research focuses on the modular addition problem, revealing that binary and ternary networks learn similar algorithms as full-precision networks, casting doubt on their potential as a more interpretable alternative in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into ways to make Large Language Models (LLMs) use less memory and work faster. They try using special kinds of transformer networks that only use 2 or 3 numbers instead of all the numbers like usual. The study tries to figure out if these new networks learn different or similar things compared to the normal ones. It does this by looking at a simple math problem called modular addition. What they found is that the new networks learn pretty much the same thing as the normal ones, which means they might not be as helpful for understanding LLMs. |
Keywords
» Artificial intelligence » Inference » Precision » Transformer
