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.

翻译：在人工智能不断发展的背景下，通过大型语言模型（LLMs）生成富有上下文且有意义的响应至关重要。研究人员越来越意识到，参数较少的LLMs在尝试为开放式问题提供合适答案时所面临的挑战。为应对这些障碍，整合尖端策略，即向LLMs增强丰富的外部领域知识，可带来显著改进。本文介绍了一种新颖框架，该框架结合了基于图驱动的上下文检索与知识图谱增强，提升了LLMs的能力，特别是在AskUbuntu、Unix和ServerFault等特定领域社区问答平台中。我们在不同参数规模的多个LLMs上进行了实验，以评估它们在开放式问题答案中锚定知识并确定事实准确性的能力。我们的方法GraphContextGen持续优于主流的基于文本的检索系统，展示了其在更多用例中的稳健性和适应性。这一进展凸显了将丰富上下文的数据检索与LLMs配对的重要性，为AI系统中的知识源获取与生成提供了一种全新方法。我们还表明，由于丰富的上下文数据检索，关键实体以及生成的答案在事实上与黄金答案保持连贯一致。