ScriptoriumWS：一个面向弱监督的代码生成助手
ScriptoriumWS: A Code Generation Assistant for Weak Supervision
AI总结：
本文提出 ScriptoriumWS，利用代码生成模型辅助编写弱监督来源，并通过多层提示及手写与生成来源结合，在保持准确率的同时显著提高覆盖率。
摘要：
弱监督是一种用于克服标注数据瓶颈（即为训练数据获取标签的需求）的流行框架。在弱监督中，会使用多个有噪声但成本低廉的来源来提供对标签的猜测，并将其聚合以生成高质量的伪标签。这些来源通常表现为领域专家编写的小程序，因此获取成本很高。相反，我们主张使用代码生成模型充当编写弱监督来源的编码助手。我们研究了用于最大化生成来源质量的提示策略，最终确定了一种融合多种类型信息的多层策略。我们探索了如何最佳地结合手写来源与生成来源。利用这些洞见，我们推出了 ScriptoriumWS，这是一个弱监督系统；与手工制作的来源相比，它能够保持准确率并大幅提升覆盖率。
英文摘要：
Weak supervision is a popular framework for overcoming the labeled data bottleneck: the need to obtain labels for training data. In weak supervision, multiple noisy-but-cheap sources are used to provide guesses of the label and are aggregated to produce high-quality pseudolabels. These sources are often expressed as small programs written by domain experts -- and so are expensive to obtain. Instead, we argue for using code-generation models to act as coding assistants for crafting weak supervision sources. We study prompting strategies to maximize the quality of the generated sources, settling on a multi-tier strategy that incorporates multiple types of information. We explore how to best combine hand-written and generated sources. Using these insights, we introduce ScriptoriumWS, a weak supervision system that, when compared to hand-crafted sources, maintains accuracy and greatly improves coverage.
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。
