Paloma: A Benchmark for Evaluating Language Model Fit
Ian Magnusson Akshita Bhagia Valentin Hofmann Luca Soldaini Ananya Harsh Jha Oyvind Tafjord Dustin Schwenk Evan Pete Walsh Yanai Elazar Kyle Lo Dirk Groeneveld Iz Beltagy Hannaneh Hajishirzi Noah A. Smith Kyle Richardson Jesse Dodge
Language models (LMs) commonly report perplexity on monolithic data held out
from training. Implicitly or explicitly, this data is composed of
domains$\unicode{x2013}$varying distributions of language. Rather than assuming
perplexity on one distribution extrapolates to others, Perplexity Analysis for
Language Model Assessment (Paloma), measures LM fit to 585 text domains,
ranging from nytimes.com to r/depression on Reddit. We invite submissions to
our benchmark and organize results by comparability based on compliance with
guidelines such as removal of benchmark contamination from pretraining.
Submissions can also record parameter and training token count to make
comparisons of Pareto efficiency for performance as a function of these
measures of cost. We populate our benchmark with results from 6 baselines
pretrained on popular corpora. In case studies, we demonstrate analyses that
are possible with Paloma, such as finding that pretraining without data beyond
Common Crawl leads to inconsistent fit to many domains.