---
title: "Who cites Fine-Tuning Language Models — 87 citing papers"
description: "87 papers of the Elamur Research library cite «Fine-Tuning Language Models with Just Forward Passes», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Fine-Tuning Language Models with Just Forward…](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) / cited by

# Who cites «Fine-Tuning Language Models with Just Forward Passes»

87 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «Fine-Tuning Language Models with Just Forward Passes»](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/)

1. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
2. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
3. **[Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://md.research.elamur.ai/free/en/papers/lv-2024-full-parameter-fine-tuning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Lv, Yuqing Yang, Tengxiao Liu, et al.*2024 · 143 citations*
4. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
5. **[RoZO: Geometry-Aware Zeroth-Order Fine-Tuning on Low-Rank Adapters for Black-Box Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2026-rozo-geometry-aware-zeroth-order/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zichen Song, Weijia Li*2026 · 1 citation*
6. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026*
7. **[Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/park-2026-memory-efficient-structured-backpropagation-device/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**JuneYoung Park, Yuri Hong, Seongwan Kim, et al.*2026*
8. **[Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation](https://md.research.elamur.ai/free/en/papers/ma-2026-private-seeds-public-llms-realistic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Ma, Sarah Rajtmajer*2026*
9. **[Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling](https://md.research.elamur.ai/free/en/papers/wang-2026-universally-empowering-zeroth-order-optimization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fei Wang, Li Shen, Liang Ding, et al.*2026*

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

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