InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models

Understand

This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs).

• Different from existing approaches that rely on extensive training with query-document pairs or retrieval-specific instructions, our method leverages the instruction-following capabilities of instruction-tuned LLMs for passage reranking without any additional fine-tuning.
• To achieve this, we introduce a soft score aggregation technique and employ pairwise reranking for unsupervised passage reranking.
• Experiments on the BEIR benchmark demonstrate that InstUPR outperforms unsupervised baselines as well as an instruction-tuned reranker, highlighting its effectiveness and superiority.