Information Extraction (IE) is an essential task in Natural Language
Processing. Traditional methods have relied on coarse-grained extraction with
simple instructions. However, with the emergence of Large Language Models
(LLMs), there is a need to adapt IE techniques to leverage the capabilities of
these models. This paper introduces a fine-grained IE benchmark dataset
tailored for LLMs, employing augmented instructions for each information type,
which includes task descriptions, extraction rules, output formats, and
examples. Through extensive evaluations, we observe that encoder-decoder
models, particularly T5 and FLAN-T5, perform well in generalizing to unseen
information types, while ChatGPT exhibits greater adaptability to new task
forms. Our results also indicate that performance is not solely dictated by
model scale, and highlight the significance of architecture, data diversity,
and learning techniques. This work paves the way for a more refined and
versatile utilization of LLMs in Information Extraction.

引入了一种为大型语言模型定制的细粒度信息抽取基准数据集，通过评估发现编码器 - 解码器模型在泛化到未见过的信息类型方面表现良好，而 ChatGPT 对于新任务形式具有更大的适应性。结果还表明，性能不仅仅取决于模型规模，还强调了架构、数据多样性和学习技术的重要性。这项工作为在信息提取中更精细、更灵活地利用大型语言模型铺平了道路。