Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction

• URL: http://arxiv.org/abs/2402.14373v1
• Date: Thu, 22 Feb 2024 08:26:56 GMT
• Title: Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction
• Authors: Xuemei Tang and Jun Wang and Qi Su
• Abstract summary: In this paper, we propose SLCoLM, a model collaboration framework, to mitigate the data long-tail problem. We use the textitTraining-Guide-Predict'' strategy to combine the strengths of pre-trained language models (PLMs) and large language models (LLMs) Our experiments on a RE dataset rich in relation types show that the approach in this paper facilitates RE of long-tail relation types.
• Score: 13.344709924683471
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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