InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
<|reference_start|>InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models: 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<|reference_end|>
@article{huang2024instupr,
    title={InstUPR : Instruction-based Unsupervised Passage Reranking with Large
  Language Models},
    author={Chao-Wei Huang and Yun-Nung Chen},
    journal={arXiv preprint arXiv:2403.16435},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16435},
    primaryClass={cs.CL cs.IR}
}