Automatic detection and remediation of contradictory natural language content in information systems

Abstract

A system and method for creating a remediation plan with in-context learning (ICL) to a large language model (LLM) involves accessing pieces of content from a corpora and calculating similarity scores between selected content and the corpora. If the similarity score exceeds a set threshold, static metadata—such as authorship, file name, file size, last updated date, or source location—is retrieved. Dynamic metadata, including word count, sentence count, grammar correctness, number of citations, or layout classification, is also generated. Based on static and dynamic metadata, a remediation action is determined, which may involve merging content, prioritizing one piece over another, or ignoring certain content. A remediation prompt is then created and sent to the LLM to generate a preferred output. The process continues by accessing additional unprocessed content and repeating until a time limit expires, computational resources are consumed, or all content has been processed.