重新构想用于回答查询的检索增强语言模型

摘要

我们对大型语言模型进行了现实检验，并检查了检索增强语言模型的潜力。这些语言模型是半参数化的，其中模型整合模型参数和来自外部数据源的知识来进行预测，与普通大型语言模型的参数化性质相对。我们提供了初步的实验结果，表明半参数化架构可以通过视图、查询分析器/规划器和来源地的增强，构建出一个在准确性和效率方面显著更强大的系统，可能适用于问答等其他自然语言处理任务。

English

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

重新构想用于回答查询的检索增强语言模型

Reimagining Retrieval Augmented Language Models for Answering Queries

摘要

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