---
title: "Who cites L4Q: Parameter Efficient — 6 citing papers"
description: "6 papers of the Elamur Research library cite «L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models», found…"
canonical: "https://research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/cited-by/"
---
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# Who cites «L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models»

6 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models»](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/)

1. [**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/)
2. **[LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation](https://md.research.elamur.ai/free/en/papers/song-2026-lbllm-lightweight-binarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siqing Song, Chuang Wang, Yong Lang, et al.*2026*
3. **[PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/mishra-2026-pe-qat-parameter-efficient-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shresth Mishra*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/) — L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models