基于多维指令集微调的大语言模型金融事件抽取OA

Event extraction is a key task in natural language processing that aims to automatically identify event triggers,event types,arguments,and argument roles from unstructured or semi-structured text and convert them into structured representations.To address the challenges faced by traditional deep learning models in extracting financial events,such as high annotation costs,low efficiency in handling long documents,and limited capability for parsing complex events,this paper proposes a large language model event extraction method based on multi-dimensional instruction set fine-tuning(MIFEE).MIFEE begins by designing a multi-dimensional instruction library that addresses various requirements of financial event extraction.It then proposes an instruction importance-driven stacking strategy,which first quantifies the independent contribution of each instruction dimension,and then stacks them in order of importance.This approach maximizes the complementary effects across dimensions while avoiding instruction redundancy,thereby constructing a multi-dimensional instruction set.Finally,the model fine-tunes a large language model based on this multi-dimensional instruction set to enhance financial event extraction performance.Experimental results show that MIFEE outperforms the compared baseline methods on the general financial event extraction datasets ChFinAnn and DuEE-Fin,achieving optimal F1 scores of 90.7%and 77.3%,respectively,demonstrating the superiority of MIFEE.In performance validation experiments conducted under long-text and complex event scenarios,MIFEE surpasses few-shot in-context learning(Few-shot ICL)methods in both F1 score and inference speed,confirming its strong capability in parsing efficiency and accuracy when handling complex semantic structures and long-range dependencies.Additionally,ablation experiments further validate the effectiveness of the instruction importance-driven stacking strategy.