---
title: "Who cites Raise a Child in Large Language — 67 citing papers"
description: "67 papers of the Elamur Research library cite «Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning»…"
canonical: "https://research.elamur.ai/free/en/papers/xu-2021-raise-child-large-language-model/cited-by/"
---
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# Who cites «Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning»

67 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 «Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning»](https://md.research.elamur.ai/free/en/papers/xu-2021-raise-child-large-language-model/)

1. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
2. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears arXiv:2304.05302**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/)
3. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
4. [**SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic Knowledge**Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zamparelli-2022-semeval-2022-task-3-pretens/)
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/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/xu-2021-raise-child-large-language-model/) — Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning