Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for pre-trained language models (PLMs) use parameter reduction techniques. |
| Approach: | They propose a pre-trained language model compression approach based on the matrix product operator from quantum many-body physics. |
| Outcome: | The proposed approach can decompose an original matrix into central tensors and auxiliary tenses . it can be applied to the original or compressed PLMs in a general way, with a lighter network . |
Similar Papers
Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to scale pre-trained language models to a deeper model depth share all parameters or use extra blocks. |
| Approach: | They propose a parameter-efficient approach to scaling pre-trained language models to a deeper model depth using matrix product operator. |
| Outcome: | The proposed model scales pre-trained language models to a deeper model depth by 4x and achieves 0.1 points higher than BERT-large for GLUE score. |
Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization (2023.acl-long)
Copied to clipboard
| Challenge: | Large pre-trained language models (PLMs) have shown remarkable performance in various natural language processing tasks, outperforming small PLMs by a large margin. |
| Approach: | They propose to scale up parameters of pre-trained language models only during fine-tuning to benefit from over-parameterization. |
| Outcome: | The proposed approach can significantly boost the fine-tuning performance of small PLMs and even help small PDMs outperform 3 parameterized larger ones. |
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (2022.coling-1)
Copied to clipboard
| Challenge: | Recent results show that the mix-of-experts architecture is parameter inefficient . large-scale pre-trained language models can achieve excellent performance in many NLP tasks. |
| Approach: | They propose to build a parameter-efficient mix-of-experts architecture by sharing information across experts. |
| Outcome: | The proposed architecture increases model capacity without increasing computation costs. |
Compressing Pre-trained Language Models by Matrix Decomposition (2020.aacl-main)
Copied to clipboard
| Challenge: | Large pre-trained language models reach state-of-the-art results when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI). |
| Approach: | They propose a two-stage model-compression method to reduce a model’s inference time cost by decompressing the model into smaller matrices and performing feature distillation on the internal representation. |
| Outcome: | The proposed method reduces the number of parameters by 0.4x and increases inference speed by 1.45x while preserving the information contained within the model. |
Low-Rank Prune-And-Factorize for Language Model Compression (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to reduce parameter redundancy in pre-processed language models fail to retain satisfactory performance under moderate to high compression rates. |
| Approach: | They propose to use network pruning to extract low-rank sparsity pattern desirable to matrix factorization. |
| Outcome: | The proposed method has a superior compression-performance trade-off compared to existing methods. |
ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations (2025.findings-acl)
Copied to clipboard
| Challenge: | Structured matrix representations of large language models are a promising way to reduce the number of parameters in natural language processing tasks but require a significant amount of computational and memory resources. |
| Approach: | They propose to utilize invariance of the network output under certain orthogonal transformations of weight matrices to identify transformations that improve compressibility of weights within structured classes. |
| Outcome: | The proposed approach is applicable to various types of structured matrices that support efficient projection operations. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation (2026.findings-acl)
Copied to clipboard
Junlin Li, Shuangyong Song, Guodong DU, Ngai Wong, Xuebo Liu, Yongxiang Li, Min Zhang, Jing Li, Xuelong Li
| Challenge: | Existing methods for fine-tuned large language models fail on fine-scale datasets . large data scale amplifies delta parameter magnitude, singular values, and entropy, causing compression errors. |
| Approach: | They propose a training- and data-free delta compression method that captures dominant delta structure and compensates residual low-rank approximation to recover fine-grained details from smaller residual error. |
| Outcome: | The proposed method outperforms existing methods on large-scale datasets on dense and MoE architectures. |
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
| Approach: | They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. |
| Outcome: | The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed. |