自引导任务识别
Self-Directed Task Identification
AI总结：
本文提出自引导任务识别框架SDTI，通过问题建模和架构设计实现零样本下模型自主识别目标变量，优于基线模型14%的F1分数，展示了减少人工标注依赖和提升自主学习系统可扩展性的潜力。
摘要：
本文提出了一种名为自引导任务识别（SDTI）的新型机器学习框架，使模型在零样本设置下能够自主识别每个数据集的正确目标变量，而无需预训练。SDTI是一个最小、可解释的框架，展示了将核心机器学习概念重新利用于新任务结构的可行性。传统方法缺乏这种能力，导致数据标注成为耗时且依赖人工的过程。仅使用标准神经网络组件，我们通过适当的问题建模和架构设计展示了SDTI的实现。我们在多个基准任务上评估了所提出的框架，并证明了其在一组潜在目标变量中可靠地识别真实值的能力。SDTI在合成任务识别基准上的F1分数比基线架构高出14%。这些概念验证实验突显了SDTI未来减少对人工标注的依赖以及增强现实应用中自主学习系统可扩展性的潜力。
英文摘要：
In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.
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。
