元提示优化的检索增强生成
Meta-prompting Optimized Retrieval-augmented Generation
AI总结：
本文提出利用元提示优化精炼检索内容，以缓解检索增强生成中内容过载与焦点分散问题，并在StrategyQA多跳问答任务上取得超过30%的性能提升。
摘要：
检索增强生成借助从外部来源检索到的内容，以提升大型语言模型在下游任务中的表现。然而，检索内容量过大、各部分可能分散或超出焦点范围，最终可能产生有害而非增益的效果。为缓解这一问题并改进检索增强生成，我们提出一种方法，在将检索内容纳入提示之前，通过元提示优化对其进行精炼。在具有挑战性的StrategyQA数据集的多跳问答任务上进行实证测试，评估结果表明，该方法相比未采用此方法的类似检索增强系统，性能提升超过30%。
英文摘要：
Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible dispersion of its parts, or their out of focus range may happen nevertheless to eventually have a detrimental rather than an incremental effect. To mitigate this issue and improve retrieval-augmented generation, we propose a method to refine the retrieved content before it is included in the prompt by resorting to meta-prompting optimization. Put to empirical test with the demanding multi-hop question answering task from the StrategyQA dataset, the evaluation results indicate that this method outperforms a similar retrieval-augmented system but without this method by over 30%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
