LMD3: Language Model Data Density Dependence
<|reference_start|>LMD3: Language Model Data Density Dependence: 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.<|reference_end|>
@article{kirchenbauer2024lmd3:,
    title={LMD3: Language Model Data Density Dependence},
    author={John Kirchenbauer, Garrett Honke, Gowthami Somepalli, Jonas Geiping,
  Daphne Ippolito, Katherine Lee, Tom Goldstein, David Andre},
    journal={arXiv preprint arXiv:2405.06331},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06331},
    primaryClass={cs.LG cs.CL}
}