Reproduce “Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Zeyuan Allen-Zhu, Yuanzhi Li
Researchers Zeyuan Allen-Zhu and Yuanzhi Li from Meta/FAIR Labs and Mohamed bin Zayed University of AI established precise scaling relationships for language model knowledge capacity, demonstrating that transformer-based models can store approximately 2 bits of factual knowledge per parameter when sufficiently trained and exposed to knowledge pieces around 1,000 times.
Key insights
- Language models exhibit a fundamental linear scaling law for knowledge capacity with parameter count.
- The knowledge capacity scaling law is largely architecture-independent when models are sufficiently trained.
- Models can learn to prioritize high-quality knowledge sources in noisy datasets if given appropriate signals.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.