Summary of Lego: Language Model Building Blocks, by Shrenik Bhansali et al.
LEGO: Language Model Building Blocks
by Shrenik Bhansali, Alwin Jin, Tyler Lizzo, Larry Heck
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed technique, LEGO, extracts small language models (SLMs) from large language models (LLMs) and recombines them to create task-specific SLM building blocks. This approach utilizes state-of-the-art LLM pruning strategies for efficient fine-tuning and inference while preserving user data privacy. The paper demonstrates the versatility of LEGO in enabling model heterogeneity, mitigating data heterogeneity effects, and maintaining robustness without high costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LEGO is a new way to make small language models from big ones. It’s like taking apart a large puzzle and reusing some of the pieces to create smaller puzzles that are just right for specific tasks. This helps keep user data private while still using powerful language models efficiently. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Pruning
