In the continuously advancing AI landscape, crafting context-rich and
meaningful responses via Large Language Models (LLMs) is essential. Researchers
are becoming more aware of the challenges that LLMs with fewer parameters
encounter when trying to provide suitable answers to open-ended questions. To
address these hurdles, the integration of cutting-edge strategies, augmentation
of rich external domain knowledge to LLMs, offers significant improvements.
This paper introduces a novel framework that combines graph-driven context
retrieval in conjunction to knowledge graphs based enhancement, honing the
proficiency of LLMs, especially in domain specific community question answering
platforms like AskUbuntu, Unix, and ServerFault. We conduct experiments on
various LLMs with different parameter sizes to evaluate their ability to ground
knowledge and determine factual accuracy in answers to open-ended questions.
Our methodology GraphContextGen consistently outperforms dominant text-based
retrieval systems, demonstrating its robustness and adaptability to a larger
number of use cases. This advancement highlights the importance of pairing
context rich data retrieval with LLMs, offering a renewed approach to knowledge
sourcing and generation in AI systems. We also show that, due to rich
contextual data retrieval, the crucial entities, along with the generated
answer, remain factually coherent with the gold answer.

通过引入图驱动的上下文检索和基于知识图谱的增强来提高大型语言模型的能力，特别是在特定领域的社区问答平台中，提供上下文丰富的数据检索与大型语言模型的配对，为 AI 系统中的知识获取和生成带来了一种新的方法。