---
title: "Who cites Improving Few-Shot Generalization — 7 citing papers"
description: "7 papers of the Elamur Research library cite «Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/albalak-2023-improving-few-shot-generalization-exploring/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/albalak-2023-improving-few-shot-generalization-exploring/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Improving Few-Shot Generalization by Exploring…](https://md.research.elamur.ai/free/en/papers/albalak-2023-improving-few-shot-generalization-exploring/) / cited by

# 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»](https://md.research.elamur.ai/free/en/papers/albalak-2023-improving-few-shot-generalization-exploring/)

1. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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 models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
4. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
5. [**Generative Reward Models arXiv:2410.12832**Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/)
6. [**Efficient Online Data Mixing For Language Model Pre-Training arXiv:2312.02406**Alon Albalak, Liangming Pan, Colin Raffel, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2023-efficient-online-data-mixing-language/)
7. [**Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models arXiv:2412.02980**Alex Havrilla, Andrew Dai, Laura O'Mahony, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-surveying-effects-quality-diversity-complexity/)
- [← back to](https://md.research.elamur.ai/free/en/papers/albalak-2023-improving-few-shot-generalization-exploring/) — Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data