Eilon Sheetrit

Ranking Passages in Relevant Documents Using LLMs

This work presents a study of ranking approaches, including lexical, dense and zero-shot prompted large language models (LLMs), to rank passages in relevant documents based on the presumed fraction of relevant text they contain, and demonstrates the merits of these approaches in utilizing relevance feedback.