Summary of Algorithmic Progress in Language Models, by Anson Ho et al.
Algorithmic progress in language models
by Anson Ho, Tamay Besiroglu, Ege Erdil, David Owen, Robi Rahman, Zifan Carl Guo, David Atkinson, Neil Thompson, Jaime Sevilla
First submitted to arxiv on: 9 Mar 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 A study analyzed the evolution of pre-training language models, tracking improvements since the advent of deep learning. The research used a dataset of over 200 language model evaluations on Wikitext and Penn Treebank spanning 2012-2023. Results showed that the compute required to reach a set performance threshold has halved approximately every 8 months, with a 95% confidence interval of around 5 to 14 months. This rapid pace is faster than hardware gains per Moore’s Law. The study also estimated augmented scaling laws, enabling quantification of algorithmic progress and determining the relative contributions of scaling models versus innovations in training algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models have improved dramatically since the introduction of deep learning. Researchers analyzed a large dataset of language model evaluations to understand how fast these improvements are happening. They found that it takes less time (about 8 months) for computers to reach a certain level of performance, and this is faster than expected based on hardware advancements. |
Keywords
» Artificial intelligence » Deep learning » Language model » Scaling laws » Tracking
