Researchers from Peking University, Meituan, Meta AI, National Key Laboratory of General Artificial Intelligence, BIGAI, and Renmin University of China have introduced a compression paradigm called Retrieval-based Knowledge Transfer (RetriKT). This approach aims to efficiently transfer information from Large Language Models (LLMs) to small-scale models. Their method involves extracting knowledge from LLMs to create a knowledge store, and then the small-scale model retrieves relevant information from the store to complete tasks. Comprehensive tests on low-resource tasks show that RetriKT significantly improves the performance of small-scale models compared to previous knowledge distillation techniques.
**Researchers from China Introduced a Novel Compression Paradigm called Retrieval-based Knowledge Transfer (RetriKT): Revolutionizing the Deployment of Large-Scale Pre-Trained Language Models in Real-World Applications**
Researchers from Peking University, Meituan, Meta AI, National Key Laboratory of General Artificial Intelligence, BIGAI, and Renmin University of China have proposed a new compression paradigm called RetriKT. This paradigm aims to efficiently and precisely transmit information from Large Language Models (LLMs) to small-scale models.
The method consists of two primary steps: extracting knowledge from the LLM to create a knowledge store, and then allowing the small-scale model to retrieve relevant information from the knowledge store to complete its task. The researchers conducted comprehensive tests on difficult and low-resource tasks from the SuperGLUE and GLUE benchmarks, and the results showed that RetriKT significantly improves the performance of small-scale models compared to earlier knowledge distillation approaches.
**Key Contributions:**
– RetriKT is a novel compression paradigm that transmits information from LLMs to small-scale models.
– The researchers carefully constructed the incentive function and proposed the reinforcement learning algorithm PPO to improve the generation quality.
– Through comprehensive tests, they improved the accuracy and diversity of knowledge collected from LLMs used for knowledge transfer, resulting in improved performance of small-scale models.
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