🔗 Who cites «Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data»

7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data»

1. TIES-Merging: Resolving Interference When Merging ModelsConference on Neural Information Processing Systems (NeurIPS)Prateek Yadav, Derek Tam, Leshem Choshen, et al.2023 · 324 citations
2. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 191 citations
3. A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & ToxicityarXiv:2305.13169Shayne Longpre, Gregory Yauney, Emily Reif, et al.2023 · 104 citations
4. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
5. Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
6. Efficient Online Data Mixing For Language Model Pre-TrainingarXiv:2312.02406Alon Albalak, Liangming Pan, Colin Raffel, et al.2023 · 35 citations
7. Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language ModelsarXiv:2412.02980Alex Havrilla, Andrew Dai, Laura O'Mahony, et al.2024 · 17 citations

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