计算机科学与探索2026，Vol.20Issue(8)：2276-2287,12.DOI:10.3778/j.issn.1673-9418.2602001
基于多维指令集微调的大语言模型金融事件抽取
Multi-dimensional Instruction Set Tuning for Financial Event Extraction with Large Language Models
摘要
Abstract
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.关键词
自然语言处理/金融事件抽取/多维指令集/大语言模型Key words
natural language processing/financial event extraction/multi-dimensional instruction set/large language models分类
信息技术与安全科学引用本文复制引用
杨维,才智杰..基于多维指令集微调的大语言模型金融事件抽取[J].计算机科学与探索,2026,20(8):2276-2287,12.基金项目
国家自然科学基金(61966031,61866032) （61966031,61866032）
青海省藏文信息处理与机器翻译重点实验室项目(2020-ZJ-Y05) （2020-ZJ-Y05）
藏文信息处理教育部重点实验室项目(2013-Z-Y17,2014-Z-Y32,2015-Z-Y03). This work was supported by the National Natural Science Foundation of China(61966031,61866032),the Program of Tibetan Informa-tion Processing and Machine Translation Key Laboratory of Qinghai Province(2020-ZJ-Y05),and the Program of Key Laboratory of Tibetan Information Processing,Ministry of Education(2013-Z-Y17,2014-Z-Y32,2015-Z-Y03). （2013-Z-Y17,2014-Z-Y32,2015-Z-Y03）
