Upload README.md with huggingface_hub

README.md

@@ -21,7 +21,7 @@ This dataset contains the results of memorization evaluations for all [Pythia](h
 
 A training sequence is considered **memorized** if, when prompted with the first 32 tokens of the sequence, the model's greedy continuation exactly matches the next 32 tokens. This is evaluated over all ~146M training sequences in the Pile.
 
-This dataset was generated for the paper [Emergent and Predictable Memorization in Large Language Models](https://arxiv.org/abs/2304.11158) (NeurIPS 2023), which studies how memorization scales with model size, training time, and data duplication across the Pythia suite.
+This dataset was generated for the paper [Emergent and Predictable Memorization in Large Language Models](https://arxiv.org/abs/2304.11158) (NeurIPS 2023). That paper introduced the study of memorization at the level of individual sequences — prior work had treated memorization as a corpus-level statistical phenomenon — and posed the problem of proactively predicting which specific sequences a model will memorize before or during training.
 
 ## Dataset Structure