تقرير
A Taxonomy for Data Contamination in Large Language Models
| العنوان: | A Taxonomy for Data Contamination in Large Language Models |
|---|---|
| المؤلفون: | Palavalli, Medha, Bertsch, Amanda, Gormley, Matthew R. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | Large language models pretrained on extensive web corpora demonstrate remarkable performance across a wide range of downstream tasks. However, a growing concern is data contamination, where evaluation datasets may be contained in the pretraining corpus, inflating model performance. Decontamination, the process of detecting and removing such data, is a potential solution; yet these contaminants may originate from altered versions of the test set, evading detection during decontamination. How different types of contamination impact the performance of language models on downstream tasks is not fully understood. We present a taxonomy that categorizes the various types of contamination encountered by LLMs during the pretraining phase and identify which types pose the highest risk. We analyze the impact of contamination on two key NLP tasks -- summarization and question answering -- revealing how different types of contamination influence task performance during evaluation. Comment: 19 pages, 8 figures, accepted to CONDA Workshop on Data Contamination @ ACL 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.08716 |
| رقم الأكسشن: | edsarx.2407.08716 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |