--- title: "Who cites Extreme Compression of Large — 80 citing papers" description: "80 papers of the Elamur Research library cite «Extreme Compression of Large Language Models via Additive Quantization», found in their…" canonical: "https://research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Extreme Compression of Large Language Models via…](https://md.research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/) / cited by # Who cites «Extreme Compression of Large Language Models via Additive Quantization» 80 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «Extreme Compression of Large Language Models via Additive Quantization»](https://md.research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/) 1. **[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,048 citations* 2. **[QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://md.research.elamur.ai/free/en/papers/ashkboos-2024-quarot-outlier-free-4-bit/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, et al.*2024 · 254 citations* 3. [**SqueezeLLM: Dense-and-Sparse Quantization arXiv:2306.07629**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/) 4. [**QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks arXiv:2402.04396**Albert Tseng, Jerry Chee, Qingyao Sun, et al.*2024 · 117 citations*](https://md.research.elamur.ai/free/en/papers/tseng-2024-quip-even-better-llm-quantization/) 5. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations* 6. **[“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization](https://md.research.elamur.ai/free/en/papers/kurtic-2025-give-me-bf16-or-give/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, et al.*2025 · 21 citations* 7. **[Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://md.research.elamur.ai/free/en/papers/xiao-2026-exploring-layer-wise-information-effectiveness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**He Xiao, Qingyao Yang, Dirui Xie, et al.*2026 · 5 citations* 8. **[SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression](https://md.research.elamur.ai/free/en/papers/yun-2026-sharvet-similarity-aware-parameter-sharing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongin Yun, Jaeri Lee, Jongjin Kim, et al.*2026 · 1 citation* 9. **[FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization](https://md.research.elamur.ai/free/en/papers/xiao-2026-faq-mitigating-quantization-error-regenerating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyang Xiao, Weiqing Li, Jinyue Guo, et al.*2026* 10. **[S2O: Early Stopping for Sparse Attention via Online Permutation](https://md.research.elamur.ai/free/en/papers/zhang-2026-s2o-early-stopping-sparse-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Zhang, Songwei Liu, Chenqian Yan, et al.*2026* 11. **[SCVQ: Sparse-Compensated Vector Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-scvq-sparse-compensated-vector-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Zhou, Yujun Diao, Zicheng Kong, et al.*2026* 69 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/) — Extreme Compression of Large Language Models via Additive Quantization