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High-quality knowledge graphs (KGs) play a crucial role in many applications. However, KGs created by automated information extraction systems can suffer from erroneous extractions or be inconsistent with provenance/source text. It is important to identify and correct such problems. In this paper, we study leveraging the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on ``open'' LLMs that can be run and trained locally, we find that few-shot approaches can yield an absolute performance gain of 2.5-3.4% over the state-of-the-art method with only 9% of training data. We examine the LLM architectures' effect and show that Decoder-Only models underperform Encoder-Decoder approaches. We also explore how model size impacts performance and counterintuitively find that larger models do not result in consistent performance gains. Our detailed analyses suggest that while LLMs can improve KG consistency, the different LLM models learn different aspects of KG consistency and are sensitive to the number of entities involved. 

Information extraction systems analyze text to produce entities and beliefs, but their output often has errors. In this paper we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors. We consider both the scenario when the provenance text is automatically found by an IE system and when it is curated by humans. We contrast consistency with credibility; define and explore consistency and repair tasks; and demonstrate a simple, yet effective and generalizable, model. We analyze these tasks and evaluate this approach on three datasets. Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features.