Papers by Joseph Jennings
Data, Data Everywhere: A Guide for Pretraining Dataset Construction (2024.emnlp-main)
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Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Bo Liu, Aastha Jhunjhunwala, Zhilin Wang, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Recent language models have impressive capabilities on a number of evaluation areas. |
| Approach: | They conduct systematic analysis of pretraining set construction to identify which methods yield the greatest gains in model accuracy. |
| Outcome: | The proposed method can be used to refine and improve a pretraining set. |
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset (2025.acl-long)
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Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings, Brandon Norick, Markus Kliegl, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Recent Common Crawl datasets remove 90% of data, limiting their suitability for long token horizon training. |
| Approach: | They propose to combine classifier ensembling, synthetic data rephrasing and heuristic filters to achieve better trade-offs between accuracy and data quantity. |
| Outcome: | The proposed model-based filtering improves MMLU by 5.6 over DCLM for 15T tokens . the full 6.3T token dataset matches DCLM on MMLO, but contains four times more unique real tokens than DCLM . |