Large Language Models (LLMs) have made remarkable strides in various tasks.
However, whether they are competitive few-shot solvers for information
extraction (IE) tasks and surpass fine-tuned small Pre-trained Language Models
(SLMs) remains an open problem. This paper aims to provide a thorough answer to
this problem, and moreover, to explore an approach towards effective and
economical IE systems that combine the strengths of LLMs and SLMs. Through
extensive experiments on eight datasets across three IE tasks, we show that
LLMs are not effective few-shot information extractors in general, given their
unsatisfactory performance in most settings and the high latency and budget
requirements. However, we demonstrate that LLMs can well complement SLMs and
effectively solve hard samples that SLMs struggle with. Building on these
findings, we propose an adaptive filter-then-rerank paradigm, in which SLMs act
as filters and LLMs act as rerankers. By utilizing LLMs to rerank a small
portion of difficult samples identified by SLMs, our preliminary system
consistently achieves promising improvements (2.1% F1-gain on average) on
various IE tasks, with acceptable cost of time and money.

通过使用自适应的筛选和重排序范式，使用大型语言模型和小型预训练语言模型相互补充，可以提高信息提取任务的性能和效率，并消减时间和预算成本。