  We introduce Venn Diagram (VD) Prompting, an innovative prompting technique
which allows Large Language Models (LLMs) to combine and synthesize information
across complex, diverse and long-context documents in knowledge-intensive
question-answering tasks. Generating answers from multiple documents involves
numerous steps to extract relevant and unique information and amalgamate it
into a cohesive response. To improve the quality of the final answer, multiple
LLM calls or pretrained models are used to perform different tasks such as
summarization, reorganization and customization. The approach covered in the
paper focuses on replacing the multi-step strategy via a single LLM call using
VD prompting. Our proposed technique also aims to eliminate the inherent
position bias in the LLMs, enhancing consistency in answers by removing
sensitivity to the sequence of input information. It overcomes the challenge of
inconsistency traditionally associated with varying input sequences. We also
explore the practical applications of the VD prompt based on our examination of
the prompt's outcomes. In the experiments performed on four public benchmark
question-answering datasets, VD prompting continually matches or surpasses the
performance of a meticulously crafted instruction prompt which adheres to
optimal guidelines and practices.
