To address the issues of the dependency on multiple specialized models and limitations on extraction attributes in the process of open source intelligence extraction,generative language model was adopted as an extraction tool and the accuracy of information extraction was improved through instruction fine-tuning and in-context learning.The SFT dataset was constructed using automated instruction generation methods to generalize the original problems.The fine-tuning was conducted for multiple tasks to learn common extraction patterns.The automatic thinking chain expansion prompts were employed to enhance the model's reasoning ability.Experimental results demonstrate that this method,in tasks such as named entity recognition,rela-tion extraction,and event extraction in open source intelligence,achieves satisfactory extraction results in various scenarios,indicating its effectiveness in extraction.
关键词
开源情报/大语言模型/信息抽取/指令自动化生成/指令微调/上下文学习/自动思维链
Key words
open source intelligence/large language model/information extraction/automatic instruction generation/instruction tuning/in-context learning/automatic chain-of-thought
维普
万方数据