  The development of Large Language Models (LLMs) has notably transformed
numerous sectors, offering impressive text generation capabilities. Yet, the
reliability and truthfulness of these models remain pressing concerns. To this
end, we investigate iterative prompting, a strategy hypothesized to refine LLM
responses, assessing its impact on LLM truthfulness, an area which has not been
thoroughly explored. Our extensive experiments delve into the intricacies of
iterative prompting variants, examining their influence on the accuracy and
calibration of model responses. Our findings reveal that naive prompting
methods significantly undermine truthfulness, leading to exacerbated
calibration errors. In response to these challenges, we introduce several
prompting variants designed to address the identified issues. These variants
demonstrate marked improvements over existing baselines, signaling a promising
direction for future research. Our work provides a nuanced understanding of
iterative prompting and introduces novel approaches to enhance the truthfulness
of LLMs, thereby contributing to the development of more accurate and
trustworthy AI systems.
