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. We employ a proxy model which has far fewer parameters, and take its answers as heuristic answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM. We only conduct retrieval for the missing knowledge in questions that the LLM does not know. Extensive experimental results on five datasets with two LLMs demonstrate a notable improvement in the end-to-end performance of LLMs in question-answering tasks, achieving or surpassing current state-of-the-art models with lower LLM inference costs.

翻译：大语言模型（LLMs）与搜索引擎的融合代表了知识获取方法论的重大演进。然而，确定LLM已具备的知识以及需要借助搜索引擎获取的知识仍是一个未解决的问题。现有方法大多通过LLM自身生成的初步答案或推理结果来解决此问题，但这会导致过高的计算成本。本文提出了一种新颖的协作方法，即SlimPLM，通过精简代理模型检测LLM中缺失的知识，从而增强LLM的知识获取过程。我们采用一个参数规模远小于LLM的代理模型，并将其回答视为启发式答案。随后，利用这些启发式答案来预测回答用户问题所需的知识，以及LLM中已知和未知的知识。我们仅对LLM未知的问题中缺失的知识进行检索。在五个数据集上使用两个LLM进行的大量实验结果表明，该方法显著提升了LLM在问答任务中的端到端性能，以更低的LLM推理成本达到或超越了当前最先进的模型。