KADR-LLM：基于深度檢索推理的大語言模型輔助檔案開放審核方法

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中圖分類號：TB9 文獻(xiàn)標(biāo)志碼：A文章編號：1674-5124（2025）07-0009-10

Abstract：This paper addresses limitations in traditional archive declassification review systems， including low efficiency， excessive subjectivity，and inadequate semantic analysis.A KADR-LLM-based intelligent auditing framework was developed，integrating Dense Passage Retrieval （DPR） capabilities with Knowledge Augmented Reasoning Process （KARP） mechanisms to establish a three-stage "retrieval-reasoningverification" paradigm. Key innovations include： A dual-channel text preprocessng method optimizing semantic representation through paragraph truncation based on document spatial structures; A rule-driven dynamic reasoning system combining sensitive term matching with retrieval-augmented generation; A keywordguided progressve auditing strategy enabling interpretable decision-making from surface feature extraction to logical chain validation. Evaluations on OParchives datasets showed KADR-LLM achieved 79.98% accuracy in zero-shot conditions and 82.34% in few-shot scenarios， surpassing baseline models by 4.31% while demonstrating superior semantic generalization capability.

Keywords： archival opening review; large language model; dense retrieval; reasoning prompt

0 引言

在數(shù)字時代，檔案開放共享對社會信息化和知識創(chuàng)新意義重大。（剩余15350字）