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（LLM）生成富有语境且具意义的回答至关重要。研究人员日益认识到，参数较少的LLM在尝试为开放式问题提供合适答案时面临的挑战。为解决这些难题，整合前沿策略——即向LLM增强丰富的外部领域知识——可带来显著改进。本文提出一种新颖框架，将图驱动的语境检索与知识图谱增强相结合，旨在提升LLM的能力，特别是在AskUbuntu、Unix和ServerFault等特定领域社区问答平台上的表现。我们对多种不同参数规模的LLM进行实验，评估其在开放式问题的答案中锚定知识并确保事实准确性的能力。我们的方法GraphContextGen在性能上持续优于主流的基于文本的检索系统，展示了其鲁棒性和对大量用例的适应性。这一进展凸显了将丰富语境的数据检索与LLM相匹配的重要性，为AI系统中的知识获取与生成提供了全新思路。我们还证明，由于丰富的语境数据检索，关键实体与生成的答案在事实上与标准答案保持一致。