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Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The integration of Large Language Models (LLMs) with Knowledge Graphs (KGs) represents a significant advancement in natural language processing, particularly in fields where accuracy is paramount, such as biomedicine. The study highlights the persistent challenge of hallucinations—instances where models produce information not grounded in actual data. By employing a query checker within the LangChain framework, the researchers ensured that the queries generated by LLMs were both syntactically and semantically valid. This methodology was rigorously tested against a benchmark dataset of 50 biomedical questions, revealing that while GPT-4 Turbo outperformed other models, open-source alternatives like llama3:70b also showed promise with proper prompt engineering. The results underscore the importance of developing reliable question-answering systems that can mitigate misinformation, especially in critical areas like healthcare, where the consequences of inaccuracies can be severe.
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