TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs

Shuyi Liu; Yu-Ming Shang; Xi Zhang

AAAI 2026technical0 citations

TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs

Abstract

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for enhancing the capabilities of Large Language Models (LLMs) by integrating retrieval-based methods with generative models. As external knowledge repositories continue to expand and the parametric knowledge within models becomes outdated, a critical challenge for RAG systems is resolving conflicts between retrieved external information and LLMs

BibTeX

@inproceedings{aaai2026_truthfulragresol,
  title = {TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs},
  author = {Shuyi Liu and Yu-Ming Shang and Xi Zhang},
  booktitle = {AAAI 2026},
  year = {2026}
}