# Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression

arXiv: https://arxiv.org/abs/2410.03765 · HTML: https://mlsanity.com/paper/2410.03765/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 45 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://mlsanity.com/paper/2306.00978/) (2023)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [A Simple and Effective Pruning Approach for Large Language Models](https://mlsanity.com/paper/2306.11695/) (2023)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [LLM-Pruner: On the Structural Pruning of Large Language Models](https://mlsanity.com/paper/2305.11627/) (2023)
- [Universal Transformers](https://mlsanity.com/paper/1807.03819/) (2018)
- [QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://mlsanity.com/paper/2404.00456/) (2024)
- [Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning](https://mlsanity.com/paper/2208.11580/) (2022)
- [Teaching Small Language Models to Reason](https://mlsanity.com/paper/2212.08410/) (2022)
- [Atom: Low-bit Quantization for Efficient and Accurate LLM Serving](https://mlsanity.com/paper/2310.19102/) (2023)
- [SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression](https://mlsanity.com/paper/2403.07378/) (2024)
- [Language model compression with weighted low-rank factorization](https://mlsanity.com/paper/2207.00112/) (2022)
- [ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models](https://mlsanity.com/paper/2312.05821/) (2023)
- [Lessons on Parameter Sharing across Layers in Transformers](https://mlsanity.com/paper/2104.06022/) (2021)
- [Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers](https://mlsanity.com/paper/2101.00234/) (2021)
- [In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models](https://mlsanity.com/paper/2212.10670/) (2022)
- [Careful Selection of Knowledge to solve Open Book Question Answering](https://mlsanity.com/paper/1907.10738/) (2019)
- [Sharing Attention Weights for Fast Transformer](https://mlsanity.com/paper/1906.11024/) (2019)
- [Lion: Adversarial Distillation of Proprietary Large Language Models](https://mlsanity.com/paper/2305.12870/) (2023)

## Built on by (most cited first, 30 of 35)

- [Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking](https://mlsanity.com/paper/2502.13842/) (2025)
- [KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs](https://mlsanity.com/paper/2604.13226/) (2026)
- [CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation](https://mlsanity.com/paper/2508.02401/) (2025)
- [CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing](https://mlsanity.com/paper/2508.16134/) (2025)
- [Generalized Fisher-Weighted SVD: Scalable Kronecker-Factored Fisher Approximation for Compressing Large Language Models](https://mlsanity.com/paper/2505.17974/) (2025)
- [Layer-wise dynamic rank for compressing large language models](https://mlsanity.com/paper/2509.25622/) (2025)
- [ERC-SVD: Error-Controlled SVD for Large Language Model Compression](https://mlsanity.com/paper/2505.20112/) (2025)
- [Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density](https://mlsanity.com/paper/2602.09316/) (2026)
- [CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning](https://mlsanity.com/paper/2509.22075/) (2025)
- [LESA: Learnable LLM Layer Scaling-Up](https://mlsanity.com/paper/2502.13794/) (2025)
- [Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning](https://mlsanity.com/paper/2508.04581/) (2025)
- [Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition](https://mlsanity.com/paper/2411.09816/) (2024)
- [A Practical Tensor-Network Compression Pipeline for Production-Scale Large Language Models](https://mlsanity.com/paper/2602.01613/) (2026)
- [Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression](https://mlsanity.com/paper/2603.18426/) (2026)
- [FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast](https://mlsanity.com/paper/2605.08314/) (2026)
- [Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression](https://mlsanity.com/paper/2604.01609/) (2026)
- [CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference](https://mlsanity.com/paper/2606.24467/) (2026)
- [Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs](https://mlsanity.com/paper/2608.08506/) (2026)
- [Neuronal Group Communication for Efficient Neural representation](https://mlsanity.com/paper/2510.16851/) (2025)
- [Rethinking Parameter Sharing as Graph Coloring for Structured Compression](https://mlsanity.com/paper/2511.06786/) (2025)
- [SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping](https://mlsanity.com/paper/2512.13494/) (2025)
- [Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression](https://mlsanity.com/paper/2605.08568/) (2026)
- [KernelDNA: Dynamic Kernel Sharing via Decoupled Naive Adapters](https://mlsanity.com/paper/2503.23379/) (2025)
- [Beyond Uniform SVD:Dual-Level Optimization across Columns and Modules for LLM Compression](https://mlsanity.com/paper/2510.19385/) (2025)
- [Decompose, Mix, Adapt: A Unified Framework for Parameter-Efficient Neural Network Recombination and Compression](https://mlsanity.com/paper/2603.27383/) (2026)
- [OptINC: Optical In-Network-Computing for Scalable Distributed Learning](https://mlsanity.com/paper/2603.28290/) (2026)
- [Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking](https://mlsanity.com/paper/2605.04971/) (2026)
- [Cross-Layer Subspace Coupling for LLM Compression: A Unifying Framework and Its Empirical Limits](https://mlsanity.com/paper/2605.30836/) (2026)
- [LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression](https://mlsanity.com/paper/2608.26389/) (2026)
- [Linear Reusable Neural Bases Architecture for Network Compression](https://mlsanity.com/paper/2609.01550/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented as a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis Sharing outperforms state-of-the-art SVD-based compression approaches and parameter sharing techniques, especially under large compression ratios. Code is available at: https://github.com/TUDa-HWAI/Basis_Sharing