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.

翻译：尽管大型语言模型（LLM）能够积累和存储知识，但它们仍然容易产生幻觉。特别是在面对事实性问题时，LLM不能仅依赖参数中存储的知识来保证真实且正确的答案。为这些模型增强搜索外部信息源（例如网络）的能力是一种有前景的方法，可将知识锚定到检索信息上。然而，在大量文档集合中搜索会引入额外的计算/时间成本。最优行为是仅在LLM对答案不自信时查询外部资源。在本文中，我们提出了一种新型LLM，能够自我评估是直接回答还是需要请求外部工具。我们通过引入幻觉屏蔽机制研究了一种监督方法，其中使用封闭式问答任务生成标签。此外，我们提出利用参数高效微调技术，在少量数据上训练我们的模型。我们的模型能直接回答78.2%的已知查询，并选择搜索77.2%的未知查询。这导致API仅在62%的情况下被使用。