Information Extraction (IE) stands as a cornerstone in natural language
processing, traditionally segmented into distinct sub-tasks. The advent of
Large Language Models (LLMs) heralds a paradigm shift, suggesting the
feasibility of a singular model addressing multiple IE subtasks. In this vein,
we introduce the General Information Extraction Large Language Model (GIELLM),
which integrates text Classification, Sentiment Analysis, Named Entity
Recognition, Relation Extraction, and Event Extraction using a uniform
input-output schema. This innovation marks the first instance of a model
simultaneously handling such a diverse array of IE subtasks. Notably, the
GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance
in integrated tasks compared to their isolated counterparts. Our experiments
demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed
datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent
evaluation using the novel Text Classification Relation and Event
Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in
text and word classification. This breakthrough paves the way for most IE
subtasks to be subsumed under a singular LLM framework. Specialized fine-tune
task-specific models are no longer needed.

信息抽取是自然语言处理中的基石，传统上被细分为不同的子任务。大型语言模型的出现预示着一种新的范式转变，即单一模型能够解决多个信息抽取子任务。本文引入通用信息抽取大型语言模型（GIELLM），它通过统一的输入输出架构整合了文本分类、情感分析、命名实体识别、关系抽取和事件抽取。这次创新标志着一个模型首次同时处理如此多样的信息抽取子任务。值得注意的是，GIELLM 利用了互相增强效应（MRE），在综合任务中提高了性能，与各自孤立的任务相比。我们的实验证明，在六个日语混合数据集中，我们在五个数据集中取得了最先进的结果，明显超过了 GPT-3.5-Turbo。此外，使用新颖的文本分类关系和事件抽取（TCREE）数据集进行的独立评估证实了 MRE 在文本和词分类中的协同优势。这一突破为大多数信息抽取子任务在一个统一的 LLM 框架下合并提供了可能，不再需要专门的微调任务特定模型。