Recently, large language models (LLMs) have been successful in relational extraction (RE) tasks, especially in the few-shot learning. An important problem in the field of RE is long-tailed data, while not much attention is paid to this problem using LLM approaches. Therefore, in this paper, we propose SLCoLM, a model collaboration framework, to mitigate the data long-tail problem. In our framework, we use the ``\textit{Training-Guide-Predict}'' strategy to combine the strengths of small pre-trained language models (SLMs) and LLMs, where a task-specific SLM framework acts as a guider, transfers task knowledge to the LLM and guides the LLM in performing RE tasks. Our experiments on an ancient Chinese RE dataset rich in relation types show that the approach facilitates RE of long-tail relation types.

翻译：近年来，大语言模型（LLMs）在关系抽取（RE）任务中取得了成功，尤其是在少样本学习场景下。关系抽取领域的一个重要问题是长尾数据分布，而现有基于大语言模型的方法对此问题关注不足。为此，本文提出SLCoLM——一种模型协作框架，以缓解数据长尾问题。在我们的框架中，采用“训练-引导-预测”策略，结合小规模预训练语言模型（SLMs）与大语言模型的优势：其中任务专用的小语言模型框架充当引导者，将任务知识迁移至大语言模型，并引导其执行关系抽取任务。我们在一个富含关系类型的古汉语关系抽取数据集上的实验表明，该方法能有效促进长尾关系类型的抽取。