Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large
  Language Models
Authors: name: Sina J. Semnani, name: Jirayu Burapacheep, name: Arpandeep Khatua, name: Thanawan Atchariyachanvanit, name: Zheng Wang, name: Monica S. Lam
Organization: Stanford Open Virtual Assistant Lab (OVAL)

Wikipedia is the largest open knowledge corpus, widely used worldwide and
serving as a key resource for training large language models (LLMs) and
retrieval-augmented generation (RAG) systems. Ensuring its accuracy is
therefore critical. But how accurate is Wikipedia, and how can we improve it?
  We focus on inconsistencies, a specific type of factual inaccuracy, and
introduce the task of corpus-level inconsistency detection. We present CLAIRE,
an agentic system that combines LLM reasoning with retrieval to surface
potentially inconsistent claims along with contextual evidence for human
review. In a user study with experienced Wikipedia editors, 87.5% reported
higher confidence when using CLAIRE, and participants identified 64.7% more
inconsistencies in the same amount of time.
  Combining CLAIRE with human annotation, we contribute WIKICOLLIDE, the first
benchmark of real Wikipedia inconsistencies. Using random sampling with
CLAIRE-assisted analysis, we find that at least 3.3% of English Wikipedia facts
contradict another fact, with inconsistencies propagating into 7.3% of FEVEROUS
and 4.0% of AmbigQA examples. Benchmarking strong baselines on this dataset
reveals substantial headroom: the best fully automated system achieves an AUROC
of only 75.1%.
  Our results show that contradictions are a measurable component of Wikipedia
and that LLM-based systems like CLAIRE can provide a practical tool to help
editors improve knowledge consistency at scale.