---
title: "Who cites Scaling Down to Scale Up: A — 61 citing papers"
description: "61 papers of the Elamur Research library cite «Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Scaling Down to Scale Up: A Guide to…](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/) / cited by

# Who cites «Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning»

61 papers of our library cite it (of the 141,840 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning»](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)

1. [**A Survey on Large Language Models for Code Generation arXiv:2406.00515**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
2. **[Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 179 citations*
3. [**ReLoRA: High-Rank Training Through Low-Rank Updates arXiv:2307.05695**Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/)
4. [**Exploring Data and Parameter Efficient Strategies for Arabic Dialect Identifications arXiv:2509.13775**Vani Kanjirangat, Ljiljana Dolamic, Fabio Rinaldi*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kanjirangat-2025-exploring-data-parameter-efficient-strategies/)
5. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)

56 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/) — Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning