---
title: "Who cites Astraios: Parameter-Efficient — 19 citing papers"
description: "19 papers of the Elamur Research library cite «Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Astraios: Parameter-Efficient Instruction Tuning…](https://md.research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/) / cited by

# Who cites «Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models»

19 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 «Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models»](https://md.research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/)

1. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*
2. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
3. **[Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 207 citations*
4. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions arXiv:2406.15877**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
5. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
6. [**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/)
7. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
8. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
9. [**Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress arXiv:2408.14960**Ayomide Odumakinde, Daniel D'souza, Pat Verga, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/odumakinde-2024-multilingual-arbitrage-optimizing-data-pools/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/) — Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models