Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction
CoRR(2024)
摘要
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
“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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