用大模型生成评分数据，让小模型用10%人工标注数据达到全量数据效果。
Cyborg Data: Merging Human with AI Generated Training Data
- 大模型为小模型生成评分数据，形成混合训练集。
- 仅用10%人工标注数据，小模型性能接近全量数据训练。
- 适合资源有限但需高精度评估的教育测评场景。
大规模评估中的自动评分系统传统上依赖小型统计模型，需大量人工评分数据以保证预测准确，但此过程耗时且成本高昂。生成式大语言模型在多任务训练后展现出强大的零样本或少样本泛化能力。尽管其推理计算开销较大，但仍需微调以满足实际应用标准。研究表明，即使在少量数据微调下，这些模型也能超越人与人之间的评分一致性。为此，我们提出一种模型蒸馏流程：由一个大模型（教师）在小部分训练数据上训练后，对剩余数据进行评分，生成的评分数据用于训练一个更小的模型（学生）。我们将这一融合人类与机器评分的数据集称为“Cyborg Data”。实验表明，学生模型在使用“Cyborg Data”训练时，性能可媲美使用全部原始数据训练的模型，同时仅需原有人工标注数据的10%。
原文摘要 · Abstract (English)
Automated scoring (AS) systems used in large-scale assessment have traditionally used small statistical models that require a large quantity of hand-scored data to make accurate predictions, which can be time-consuming and costly. Generative Large Language Models are trained on many tasks and have shown impressive abilities to generalize to new tasks with little to no data. While these models require substantially more computational power to make predictions, they still require some fine-tuning to meet operational standards. Evidence suggests that these models can exceed human-human levels of agreement even when fine-tuned on small amounts of data. With this in mind, we propose a model distillation pipeline in which a large generative model, a Teacher, teaches a much smaller model, a Student. The Teacher, trained on a small subset of the training data, is used to provide scores on the remaining training data, which is then used to train the Student. We call the resulting dataset "Cyborg Data", as it combines human and machine-scored responses. Our findings show that Student models trained on "Cyborg Data" show performance comparable to training on the entire dataset, while only requiring 10% of the original hand-scored data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。