Traceable LLM-based validation of statements in knowledge graphs
<|reference_start|>Traceable LLM-based validation of statements in knowledge graphs: This article presents a method for verifying RDF triples using LLMs, with an emphasis on providing traceable arguments. Because the LLMs cannot currently reliably identify the origin of the information used to construct the response to the user query, our approach is to avoid using internal LLM factual knowledge altogether. Instead, verified RDF statements are compared to chunks of external documents retrieved through a web search or Wikipedia. To assess the possible application of this workflow on biosciences content, we evaluated 1,719 positive statements from the BioRED dataset and the same number of newly generated negative statements. The resulting precision is 88%, and recall is 44%. This indicates that the method requires human oversight. We demonstrate the method on Wikidata, where a SPARQL query is used to automatically retrieve statements needing verification. Overall, the results suggest that LLMs could be used for large-scale verification of statements in KGs, a task previously unfeasible due to human annotation costs.<|reference_end|>
@article{adam2024traceable,
    title={Traceable LLM-based validation of statements in knowledge graphs},
    author={Daniel Adam and Tom'av{s} Kliegr},
    journal={arXiv preprint arXiv:2409.07507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07507},
    primaryClass={cs.AI cs.LG}
}