Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker

• URL: http://arxiv.org/abs/2305.13729v1
• Date: Tue, 23 May 2023 06:35:33 GMT
• Title: Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
• Authors: Sukmin Cho, Soyeong Jeong, Jeongyeon Seo and Jong C. Park
• Abstract summary: Large-scale language model (LLM) is utilized as a zero-shot re-ranker with excellent results. 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. We propose a novel discrete prompt optimization method, Constrained Prompt generation (Co-Prompt), with the metric estimating the optimum for re-ranking.
• Score: 0.2580765958706853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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