EAGER: Using Large Language Models to Model Threats to Sensitive Information

EAGER：使用大型语言模型对敏感信息的威胁进行建模

• 批准号: 2331492
• 负责人: Matthew Connelly
• 金额: $ 30万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331492&HistoricalAwards=false
• 关键词: EAGER; Using; Large; Language; Models

The review process for releasing government records can be time-consuming and error prone. Large Language Models could help reviewers determine whether information is already in the public domain. By developing a prototype system and measuring performance at different stages, this project aims to estimate the additional data and training required to achieve acceptable levels of accuracy. The iterative nature of the system and the involvement of domain experts allows for measuring and minimizing “hallucination.”The project decouples the reasoning ability of Large Language Models from knowledge databases. It develops a semantic query engine optimized for accurate extraction of relevant information. The project also takes an active approach to fine-tuning, whereby domain experts train a model that generates queries to retrieve records from the knowledgebase, and allows them to fine tune the retrieval engines by assessing the passages that are extracted from these records before they are fed into the Large Language Model for analysis. The output includes text descriptions of what is found through record assembly, accompanied by the records themselves for further evaluation and fine-tuning. Recently released records will serve as test data, with experts categorizing the information as new or already known. Performance metrics are analyzed, considering the impact of data size and composition on accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

发布政府记录的审查过程可能是耗时的，并且容易出错。大型语言模型可以帮助审阅者确定信息是否已经在公共领域中。通过开发原型系统并在不同阶段衡量性能，该项目旨在估计获得可接受的准确性水平所需的其他数据和培训。系统的迭代性质和领域专家的参与可以衡量和最大程度地减少“幻觉”。该项目将大语言模型的推理能力从知识数据库中脱颖而出。它开发了优化的语义查询引擎，以准确提取相关信息。该项目还采用了一种积极的方法进行微调，域专家培训了一个模型，该模型生成查询以从知识库中检索记录，并允许他们通过评估从这些记录中提取的段落来微调检索引擎，然后才能将其馈入大语言模型进行分析。该输出包括通过记录组件发现的文本描述，这些内容由记录本身完成，以进行进一步的评估和微调。最近发布的记录将用作测试数据，专家将信息归类为新的或已知的信息。考虑到数据规模和组成对准确性的影响，对性能指标进行了分析。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。