Citation-Grounded Code Comprehension: Preventing LLM Hallucination Through Hybrid Retrieval and Graph-Augmented Context

The article focuses on mitigating the hallucination problem in Large Language Models (LLMs) when dealing with code comprehension. It proposes a method that combines retrieval techniques and graph-based context augmentation to improve the accuracy and reliability of LLMs in understanding code. The use of citation grounding suggests a focus on verifiable information and reducing the generation of incorrect or unsupported statements.