Andrew Nystrom


2023

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KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals
Sandeep Silwal | Sara Ahmadian | Andrew Nystrom | Andrew Mccallum | Deepak Ramachandran | Mehran Kazemi
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

2022

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Deduplicating Training Data Makes Language Models Better
Katherine Lee | Daphne Ippolito | Andrew Nystrom | Chiyuan Zhang | Douglas Eck | Chris Callison-Burch | Nicholas Carlini
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets—for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times. Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer training steps to achieve the same or better accuracy. We can also reduce train-test overlap, which affects over 4% of the validation set of standard datasets, thus allowing for more accurate evaluation. Code for deduplication is released at https://github.com/google-research/deduplicate-text-datasets.