Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction
<|reference_start|>Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction: 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.<|reference_end|>
@article{tang2024small,
    title={Small Language Model Is a Good Guide for Large Language Model in Chinese
  Entity Relation Extraction},
    author={Xuemei Tang and Jun Wang and Qi Su},
    journal={arXiv preprint arXiv:2402.14373},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14373},
    primaryClass={cs.CL}
}