---
title: "Who cites Few-shot Fine-tuning vs. — 68 citing papers"
description: "68 papers of the Elamur Research library cite «Few-shot Fine-tuning vs. Among them: Fine-tuning Aligned Language Models Compromises…"
canonical: "https://research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/cited-by/)

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# Who cites «Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation»

68 papers of our library cite it (of the 141,840 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation»](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/)

1. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv:2310.03693**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 331 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/) **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 134 citations* **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 75 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) — Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation