Summary of Llms Learn Governing Principles Of Dynamical Systems, Revealing An In-context Neural Scaling Law, by Toni J.b. Liu et al.
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
by Toni J.B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
First submitted to arxiv on: 1 Feb 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 research paper explores the surprising ability of large language models (LLMs) to perform zero-shot time-series forecasting, specifically in dynamical systems governed by physical principles. The study focuses on LLaMA 2, a text-trained model that accurately predicts system behavior without fine-tuning or prompt engineering. The results show an increase in accuracy with longer input context windows, demonstrating an in-context version of the neural scaling law. Additionally, the paper presents an efficient algorithm for extracting probability density functions of multi-digit numbers from LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do amazing things, like predict what will happen next in a time series. But how they do it is still a mystery. Researchers studied one of these models, called LLaMA 2, and found that it’s really good at predicting the behavior of physical systems – like how particles move or how temperatures change. The model didn’t need to be trained specifically for this task, and it got better as it had more information to work with. This is important because it helps us understand how these powerful models work. |
Keywords
* Artificial intelligence * Fine tuning * Llama * Probability * Prompt * Time series * Zero shot
