bibtext: @inproceedings{
  kirchenbauer2024lmd,
  title={{LMD}3: Language Model Data Density Dependence},
  author={John Kirchenbauer and Garrett Honke and Gowthami Somepalli and Jonas Geiping and Katherine Lee and Daphne Ippolito and Tom Goldstein and David Andre},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=eGCw1UVOhk}
  }
abstract: We develop a methodology for analyzing language model task performance at the individual example level based on training data density estimation. Experiments with paraphrasing as a controlled intervention on finetuning data demonstrate that increasing the support in the training distribution for specific test queries results in a measurable increase in density, which is also a significant predictor of the performance increase caused by the intervention. Experiments with pretraining data demonstrate that we can explain a significant fraction of the variance in model perplexity via density measurements. We conclude that our framework can provide statistical evidence of the dependence of a target model’s predictions on subsets of its training data, and can more generally be used to characterize the support (or lack thereof) in the training data for a given test task.
title: LMD3: Language Model Data Density Dependence
authors: John Kirchenbauer Garrett Honke Gowthami Somepalli Jonas Geiping Katherine Lee Daphne Ippolito Tom Goldstein David Andre