提示词作为自动优化的训练超参数：仅用10个黄金标签从头训练一流IR模型
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
AI总结：
本文提出将LM提示词作为可自动优化的训练超参数，仅用10个黄金标签即可训练小规模IR模型，并在BIRCO上媲美使用10万余标签训练的7B模型。
摘要：
我们开发了一种方法，可仅用少至10个黄金相关性标签来训练小规模（参数量低于100M）神经信息检索模型。该方法依赖于使用语言模型（LM）为文档生成合成查询，关键步骤是根据训练质量自动优化用于生成这些查询的LM提示词。在BIRCO基准测试的实验中，我们发现用本方法训练的模型优于RankZephyr，并与RankLLama具有竞争力；后两者均为在超过100K个标签上训练的7B参数模型。这些发现表明自动提示词优化在合成数据集生成方面具有强大能力。
英文摘要：
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating synthetic queries for documents using a language model (LM), and the key step is that we automatically optimize the LM prompt that is used to generate these queries based on training quality. In experiments with the BIRCO benchmark, we find that models trained with our method outperform RankZephyr and are competitive with RankLLama, both of which are 7B parameter models trained on over 100K labels. These findings point to the power of automatic prompt optimization for synthetic dataset generation.
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。
