Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs

Understand

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies.

• However, determining the knowledge that an LLM already possesses and the knowledge that requires the help of a search engine remains an unresolved issue.
• Most existing methods solve this problem through the results of preliminary answers or reasoning done by the LLM itself, but this incurs excessively high computational costs.
• This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in LLMs with a slim proxy model, to enhance the LLM's knowledge acquisition process.