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- W4385571377 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." @default.
- W4385571377 created "2023-08-05" @default.
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- W4385571377 date "2023-01-01" @default.
- W4385571377 modified "2023-09-24" @default.
- W4385571377 title "Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker" @default.
- W4385571377 doi "https://doi.org/10.18653/v1/2023.findings-acl.61" @default.
- W4385571377 hasPublicationYear "2023" @default.
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