We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP tasks

翻译：我们对大语言模型进行现实检验，并考察检索增强语言模型与之相比的潜力。这类语言模型是半参数化的，即模型整合了模型参数和来自外部数据源的知识来进行预测，这与传统大语言模型的纯参数化特性形成对比。我们给出初步实验发现：通过引入视图、查询分析器/规划器以及溯源机制，半参数化架构能够显著提升系统在问答任务中的准确性和效率，并可能惠及其他自然语言处理任务。