Discrete Prompt Optimization via Constrained Generation for Zero-shot
  Re-ranker
Sukmin Cho Soyeong Jeong Jeongyeon Seo Jong C. Park
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.