Summary of Predicting Emergent Capabilities by Finetuning, By Charlie Snell et al.
Predicting Emergent Capabilities by Finetuning
by Charlie Snell, Eric Wallace, Dan Klein, Sergey Levine
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 paper tackles the challenge of understanding emergent capabilities in large language model (LLM) scaling. While the pretraining loss is predictable, downstream capabilities are less so, with sometimes dramatic jumps. The task is to predict whether future models will have non-trivial accuracy on a given task. The researchers propose an approach that finetunes LLMs on varying amounts of data and uses parametric functions to predict emergence (emergence laws). They validate this approach using four NLP benchmarks, accurately predicting emergence in some cases with up to 4x more compute. This has implications for realistic use cases such as language model selection or hyperparameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about understanding how big language models work and why they get better over time. Right now, it’s hard to predict when a new model will be good at doing certain tasks. The researchers found that if you train a small model on a little bit of data, then add more data, the point where it starts getting really good moves towards smaller models. They used this idea to create a formula that predicts when a new model will be good at something. They tested this formula with four big language model tasks and were able to predict when a new model would be good even if it had much less training than before. |
Keywords
» Artificial intelligence » Hyperparameter » Language model » Large language model » Nlp » Pretraining
