Navigating Uncertainty: Optimizing API Dependency for Hallucination
  Reduction in Closed-Book Question Answering
Pierre Erbacher Louis Falissar Vincent Guigue Laure Soulier
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.