While Large Language Models (LLM) are able to accumulate and restore
knowledge, they are still prone to hallucination. Especially when faced with
factual questions, LLM cannot only rely on knowledge stored in parameters to
guarantee truthful and correct answers. Augmenting these models with the
ability to search on external information sources, such as the web, is a
promising approach to ground knowledge to retrieve information. However,
searching in a large collection of documents introduces additional
computational/time costs. An optimal behavior would be to query external
resources only when the LLM is not confident about answers. In this paper, we
propose a new LLM able to self-estimate if it is able to answer directly or
needs to request an external tool. We investigate a supervised approach by
introducing a hallucination masking mechanism in which labels are generated
using a close book question-answering task. In addition, we propose to leverage
parameter-efficient fine-tuning techniques to train our model on a small amount
of data. Our model directly provides answers for $78.2\%$ of the known queries
and opts to search for $77.2\%$ of the unknown ones. This results in the API
being utilized only $62\%$ of the time.

我们提出了一种新的大型语言模型，能够自我评估是否能直接回答问题或需要请求外部工具。我们通过引入幻觉屏蔽机制的监督方法进行研究，并提出利用参数高效微调技术在少量数据上训练模型。我们的模型直接回答已知查询的问题，对于未知查询进行搜索，从而只在 62% 的时间内使用 API。