Improving Passage Ranking with Effective Demonstration Selection for Large Language Models

Selecting appropriate in-context demonstrations is crucial for enhancing the performance of large language models (LLMs) in passage ranking tasks. The proposed DemoRank framework addresses this challenge by introducing a demonstration retriever and a dependency-aware demonstration reranker to iteratively select the most suitable demonstrations for few-shot in-context learning.