Related papers: Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation

Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation

URL: http://arxiv.org/abs/2309.10677v2
Date: Wed, 27 Sep 2023 01:15:49 GMT
Title: Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation
Authors: Yucheng Li
Abstract summary: We propose a novel method to quantify contamination without the access of the full training set. Our analysis provides evidence of significant memorisation of recent foundation models in popular reading comprehension, summarisation benchmarks, while multiple choice appears less contaminated.
Score: 2.4173424114751114
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Data contamination in model evaluation is getting increasingly prevalent as the massive training corpora of large language models often unintentionally include benchmark samples. Therefore, contamination analysis has became an inevitable part of reliable model evaluation. However, existing method of contamination analysis requires the access of the entire training data which is often confidential for recent models. This prevent the community to rigorously audit these models and conduct accurate assessment of their capability. In this paper, we propose a novel method to quantify contamination without the access of the full training set, that measure the extent of contamination with perplexity. Our analysis provides evidence of significant memorisation of recent foundation models in popular reading comprehension, summarisation benchmarks, while multiple choice appears less contaminated.

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