The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued utility of knowledge extraction for question answering. This paper investigates the value of triple extraction in this new paradigm by extending an existing benchmark with knowledge extraction annotations and evaluating commercial and open-source LLMs of varying sizes. Our results show that web-scale knowledge extraction remains a challenging task for LLMs. Despite achieving high QA accuracy, LLMs can still benefit from knowledge extraction, through augmentation with extracted triples and multi-task learning. These findings provide insights into the evolving role of knowledge triple extraction in web-based QA and highlight strategies for maximizing LLM effectiveness across different model sizes and resource settings.

翻译：大型语言模型（LLMs）的出现显著提升了基于半结构化内容的网络问答（QA）系统性能，这引发了关于知识提取在问答任务中持续效用的思考。本文通过扩展现有基准测试并添加知识提取标注，评估了不同规模的商业及开源LLMs，以此探究三元组提取在这一新范式中的价值。研究结果表明，网络规模的知识提取对LLMs而言仍具挑战性。尽管LLMs在问答准确率上表现优异，但通过三元组提取增强与多任务学习，它们仍能从知识提取中获益。这些发现揭示了知识三元组提取在网络问答中不断演变的作用，并为在不同模型规模与资源条件下最大化LLMs效能提供了策略指引。