Summary of Llm Circuit Analyses Are Consistent Across Training and Scale, by Curt Tigges et al.
LLM Circuit Analyses Are Consistent Across Training and Scale
by Curt Tigges, Michael Hanna, Qinan Yu, Stella Biderman
First submitted to arxiv on: 15 Jul 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 The study investigates how large language models (LLMs) evolve internally as they’re trained, focusing on decoder-only LLMs with varying parameter sizes. The research tracks how mechanisms emerge and change across 300 billion tokens of training data. Results show that task abilities and supporting components develop similarly at specific token counts regardless of model scale. Additionally, the algorithms and component types involved remain consistent despite changes in attention heads over time. These findings suggest that analyzing small models at pre-training’s end can provide insights applicable even after additional training and across different model sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better all the time! This study looks at how these models change as they’re trained, focusing on special types of models called decoder-only LLMs. The researchers followed these models’ development over 300 billion tokens of text and found some interesting patterns. No matter how big or small the model was, certain skills and features emerged at similar points in training. This means that scientists can learn about large language models by studying smaller ones, even if they’re trained differently later on. |
Keywords
* Artificial intelligence * Attention * Decoder * Token
