Summary of How Predictable Is Language Model Benchmark Performance?, by David Owen
How predictable is language model benchmark performance?
by David Owen
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed research investigates the relationship between large language model performance and compute scaling across various model architectures and benchmarks. The study reveals that average benchmark performance can be decently predictable as a function of training compute scale, with an average absolute error of 6 percentage points when extrapolating BIG-Bench Hard performance across one order of magnitude in compute. However, predicting individual task performance remains challenging, with average errors reaching 18 percentage points. Nonetheless, the findings suggest that compute scaling provides a promising basis to forecast AI capabilities in diverse benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. Researchers want to know how well these models will do when they have more or less computer power. They tested many different model types on lots of tasks and found that if you know how much computer power each model had, you could make pretty good guesses about how well it would do on other tasks. But it’s still tricky to predict exactly how well a model will do on a specific task. |
Keywords
* Artificial intelligence * Large language model
