Re-rankers, which order retrieved documents with respect to the relevance score on the given query, have gained attention for the information retrieval (IR) task. Rather than fine-tuning the pre-trained language model (PLM), the large-scale language model (LLM) is utilized as a zero-shot re-ranker with excellent results. While LLM is highly dependent on the prompts, the impact and the optimization of the prompts for the zero-shot re-ranker are not explored yet. Along with highlighting the impact of optimization on the zero-shot re-ranker, we propose a novel discrete prompt optimization method, Constrained Prompt generation (Co-Prompt), with the metric estimating the optimum for re-ranking. Co-Prompt guides the generated texts from PLM toward optimal prompts based on the metric without parameter update. The experimental results demonstrate that Co-Prompt leads to outstanding re-ranking performance against the baselines. Also, Co-Prompt generates more interpretable prompts for humans against other prompt optimization methods.

翻译：重排序器根据给定查询的相关性分数对检索到的文档进行排序，在信息检索任务中受到关注。不同于微调预训练语言模型，大型语言模型作为零样本重排序器取得了优异的结果。尽管大型语言模型高度依赖提示，但提示对零样本重排序器的影响及其优化尚未被探索。在强调优化对零样本重排序器影响的同时，我们提出了一种新颖的离散提示优化方法——约束提示生成，该方法结合评估重排序最优性的指标。约束提示生成基于该指标引导预训练语言模型生成的文本朝向最优提示方向，且无需参数更新。实验结果表明，约束提示生成在重排序性能上显著优于基线方法。此外，与其他提示优化方法相比，约束提示生成能够生成对人类更具可解释性的提示。