Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
This addresses deployment challenges for LLMs in resource-constrained settings, representing a novel compression paradigm rather than an incremental improvement.
The paper tackles the problem of deploying large language models (LLMs) in real-world applications by proposing Retrieval-based Knowledge Transfer (RetriKT) to compress LLMs to extremely small scales (e.g., 1%), achieving significant performance enhancements on low-resource tasks from SuperGLUE and GLUE benchmarks.
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.