InstUPR : Instruction-based Unsupervised Passage Reranking with Large
  Language Models
Chao-Wei Huang Yun-Nung Chen
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. Source code to reproduce all experiments is
open-sourced at https://github.com/MiuLab/InstUPR