Technological Advantages and Innovations
SimpleDeepSearcher outperforms traditional Retrieval Augmented Generation (RAG) methods in several ways, especially in terms of computational efficiency.
- Training on small amounts of data: Supervised fine-tuning can be accomplished with only a small amount of selected data (e.g., 871 high-quality samples), dramatically reducing training resource requirements.
- Knowledge Distillation Applications: High-quality training data is generated by using powerful pre-trained models (e.g., LLaMA or GPT series) as teacher models through knowledge distillation techniques, which enhances the learning efficiency of the models.
- Compatibility Advantage: Supports the existing underlying large language model and dialog model without additional fine-tuning of cold-start commands, reducing deployment costs.
These innovations enable SimpleDeepSearcher to significantly reduce training and deployment costs while maintaining high performance, making it more suitable for real-world scenarios with limited resources than traditional RAG or reinforcement learning methods.
This answer comes from the articleSimpleDeepSearcher: An Intelligent Retrieval Tool for Augmenting Large Language Models with Web SearchThe
May not be reproduced without permission:AI productivity tools " SimpleDeepSearcher has significant computational efficiency advantages over traditional RAG methods
