如何为未来G发现知识：面向O-RAN的上下文RAG和LLM提示
How to Discover Knowledge for FutureG: Contextual RAG and LLM Prompting for O-RAN
AI总结：
本文提出上下文RAG方法，通过引导文档检索和上下文增强LLM性能，提升ORAN领域问答的准确性和效率，同时保持低运行时间和碳排放。
摘要：
我们提出了一种适用于5G/6G网络的检索增强型问答框架，其中开放无线电接入网络（O-RAN）已成为分散化、虚拟化和AI驱动的无线系统的核心。尽管O-RAN能够实现多厂商互操作性和云原生部署，但其快速变化的规范和接口对研究人员和实践者构成了重大挑战。手动导航这些复杂文档是劳动密集型且容易出错的，会减缓系统设计、集成和部署。为了解决这一挑战，我们采用上下文检索增强生成（上下文RAG），这是一种策略，其中候选答案选项指导文档检索和特定片段的上下文以提高大语言模型（LLM）的性能。这种改进传统RAG的方法实现了更精确和上下文感知的检索，这可以提高传递给LLM的文档的相关性，特别是在查询本身缺乏足够的上下文以准确定位时。我们的框架设计用于动态领域，其中数据迅速变化，模型必须持续更新或重新部署，而无需对LLM进行微调。我们使用ORANBenchmark-13K数据集评估了该框架，并在直接问答（直接Q&A）和思维链（CoT）提示策略下比较了三种LLM，即Llama3.2、Qwen2.5-7B和Qwen3.0-4B。我们证明，上下文RAG在标准RAG和基础提示策略上始终提高了准确性，同时保持了具有竞争力的运行时间和二氧化碳排放量。这些结果突显了上下文RAG作为在ORAN和更广泛的5G/6G环境中用于领域特定问答的可扩展且有效解决方案的潜力，能够更准确地解释演进的标准，同时保持效率和可持续性。
英文摘要：
We present a retrieval-augmented question answering framework for 5G/6G networks, where the Open Radio Access Network (O-RAN) has become central to disaggregated, virtualized, and AI-driven wireless systems. While O-RAN enables multi-vendor interoperability and cloud-native deployments, its fast-changing specifications and interfaces pose major challenges for researchers and practitioners. Manual navigation of these complex documents is labor-intensive and error-prone, slowing system design, integration, and deployment. To address this challenge, we adopt Contextual Retrieval-Augmented Generation (Contextual RAG), a strategy in which candidate answer choices guide document retrieval and chunk-specific context to improve large language model (LLM) performance. This improvement over traditional RAG achieves more targeted and context-aware retrieval, which improves the relevance of documents passed to the LLM, particularly when the query alone lacks sufficient context for accurate grounding. Our framework is designed for dynamic domains where data evolves rapidly and models must be continuously updated or redeployed, all without requiring LLM fine-tuning. We evaluate this framework using the ORANBenchmark-13K dataset, and compare three LLMs, namely, Llama3.2, Qwen2.5-7B, and Qwen3.0-4B, across both Direct Question Answering (Direct Q&A) and Chain-of-Thought (CoT) prompting strategies. We show that Contextual RAG consistently improves accuracy over standard RAG and base prompting, while maintaining competitive runtime and CO2 emissions. These results highlight the potential of Contextual RAG to serve as a scalable and effective solution for domain-specific Q&A in ORAN and broader 5G/6G environments, enabling more accurate interpretation of evolving standards while preserving efficiency and sustainability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
