gzip Predicts Data-dependent Scaling Laws

• URL: http://arxiv.org/abs/2405.16684v1
• Date: Sun, 26 May 2024 20:33:08 GMT
• Title: gzip Predicts Data-dependent Scaling Laws
• Authors: Rohan Pandey,
• Abstract summary: We generate training datasets of varying complexities by modulating the syntactic properties of a PCFG. We propose a new data-dependent scaling law for LM's that accounts for the training data's gzip-compressibility.
• Score: 2.5461535398221478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Past work has established scaling laws that predict the performance of a neural language model (LM) as a function of its parameter count and the number of tokens it's trained on, enabling optimal allocation of a fixed compute budget. Are these scaling laws agnostic to training data as some prior work suggests? We generate training datasets of varying complexities by modulating the syntactic properties of a PCFG, finding that 1) scaling laws are sensitive to differences in data complexity and that 2) gzip, a compression algorithm, is an effective predictor of how data complexity impacts scaling properties. We propose a new data-dependent scaling law for LM's that accounts for the training data's gzip-compressibility; its compute-optimal frontier increases in dataset size preference (over parameter count preference) as training data becomes harder to compress.

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