Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression

• URL: http://arxiv.org/abs/2310.15594v1
• Date: Tue, 24 Oct 2023 07:58:20 GMT
• Title: Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
• Authors: Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Xunliang Cai, Dongyan Zhao, Ran Lucien Wang, Rui Yan
• Abstract summary: Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
• Score: 64.07696663255155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. While numerous model compression techniques have been proposed, most of them are not well-suited for achieving extreme model compression when there is a significant gap in model scale. In this paper, we introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT), which effectively transfers the knowledge of LLMs to extremely small-scale models (e.g., 1%). In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference. To improve the quality of the model, soft prompt tuning and Proximal Policy Optimization (PPO) reinforcement learning techniques are employed. Extensive experiments are conducted on low-resource tasks from SuperGLUE and GLUE benchmarks. The results demonstrate that the proposed approach significantly enhances the performance of small-scale models by leveraging the knowledge from LLMs.

Related papers

• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Contemporary Model Compression on Large Language Models Inference [7.307436175842646]
  Large Language Models (LLMs) have revolutionized natural language processing by achieving state-of-the-art results across a variety of tasks. The computational demands of LLM inference, including high memory consumption and slow processing speeds, pose significant challenges for real-world applications. This survey explores techniques in model compression that address these challenges by reducing the size and computational requirements of LLMs.
  arXiv  Detail & Related papers  (2024-09-03T15:35:01Z)
• LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
  In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch. Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process. By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
  arXiv  Detail & Related papers  (2024-07-28T06:10:47Z)
• Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models [0.0]
  An increase in the number of connected devices around the world warrants compressed models that can be easily deployed at the local devices with low compute capacity and power accessibility. We implemented both, quantization and pruning, compression techniques on popular deep learning models used in the image classification, object detection, language models and generative models-based problem statements.
  arXiv  Detail & Related papers  (2024-07-22T14:20:53Z)
• SLMRec: Empowering Small Language Models for Sequential Recommendation [38.51895517016953]
  Sequential Recommendation task involves predicting the next item a user is likely to interact with, given their past interactions. Recent research demonstrates the great impact of LLMs on sequential recommendation systems. Due to the huge size of LLMs, it is inefficient and impractical to apply a LLM-based model in real-world platforms.
  arXiv  Detail & Related papers  (2024-05-28T07:12:06Z)
• Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models [90.14693869269519]
  MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. This paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques.
  arXiv  Detail & Related papers  (2024-02-22T18:56:07Z)
• Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers [2.1165011830664673]
  Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. The sheer size of these models poses challenges in terms of storage, training and inference due to the inclusion of billions of parameters through layer stacking. We show that even with fewer layers, LLMs maintain similar or better performance levels, particularly in prompt-based fine-tuning for text classification tasks.
  arXiv  Detail & Related papers  (2024-02-18T20:47:10Z)
• A Survey on Model Compression for Large Language Models [21.768293256849113]
  Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use. Model compression has emerged as a key research area to address these challenges.
  arXiv  Detail & Related papers  (2023-08-15T08:31:05Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.