Summary of Physics in Next-token Prediction, by Hongjun An and Yiliang Song and Xuelong Li
Physics in Next-token Prediction
by Hongjun An, Yiliang Song, Xuelong Li
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 authors uncover the underlying physics in Next-token Prediction (NTP) by identifying the law of information conservation within NTP. They propose two laws: the First Law of Information Capacity (IC-1), which explains intelligence emergence in auto-regressive models as a process of information transfer, and the Second Law of Information Capacity (IC-2), which establishes the relationship between model training and energy consumption. The authors also introduce Landauer’s Principle into NTP and present several corollaries with practical significance for production practices. Furthermore, they demonstrate consistency between their findings and existing Scaling Laws for Neural Language Models, Knowledge Capacity Scaling Laws, and Precision Scaling Laws. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a hidden rule in how computers predict the next word (Next-token Prediction). They figure out that this process is related to the way information flows through computer models. They also show that there’s an energy cost to training these models. The authors use this discovery to create rules for making these models more efficient and practical. |
Keywords
» Artificial intelligence » Precision » Scaling laws » Token
