Small Language Model Is a Good Guide for Large Language Model in Chinese
  Entity Relation Extraction
Xuemei Tang Jun Wang Qi Su
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
currently 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
pre-trained language models (PLMs) and LLMs, where a task-specific PLM
framework acts as a tutor, transfers task knowledge to the LLM, and guides the
LLM in performing RE tasks. Our experiments on a RE dataset rich in relation
types show that the approach in this paper facilitates RE of long-tail relation
types.