Summary of A Simple Model Of Inference Scaling Laws, by Noam Levi
A Simple Model of Inference Scaling Laws
by Noam Levi
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
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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 proposed statistical ansatz based on memorization is introduced to study neural scaling laws in the context of inference, particularly how performance improves with repeated attempts in large language models (LLMs) on reasoning tasks. The coverage metric, measuring the chance of success over repeated attempts, exhibits a functional form that aligns with observed inference scaling behavior in LLMs. An “inference loss” is defined, showing power law decay as the number of trials increases, and its connection to prompting costs is explored. Experiments on a simple generative model confirm the framework’s predictions, aligning with empirical coverage curves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well language models work when we ask them questions multiple times. It shows that the more we ask, the better they do, and it figures out why this happens. The researchers use a special way of thinking about this to create a formula that predicts what will happen. They test this formula with simple games and find that it matches what really happens. |
Keywords
» Artificial intelligence » Generative model » Inference » Prompting » Scaling laws
